an automated diagnostic tool for predicting anatomical response to
TRANSCRIPT
University of Tennessee, KnoxvilleTrace: Tennessee Research and CreativeExchange
Doctoral Dissertations Graduate School
12-2009
An Automated Diagnostic Tool for PredictingAnatomical Response to Radiation TherapyCarley Elizabeth HarrisUniversity of Tennessee - Knoxville
This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has beenaccepted for inclusion in Doctoral Dissertations by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For moreinformation, please contact [email protected].
Recommended CitationHarris, Carley Elizabeth, "An Automated Diagnostic Tool for Predicting Anatomical Response to Radiation Therapy. " PhD diss.,University of Tennessee, 2009.https://trace.tennessee.edu/utk_graddiss/606
To the Graduate Council:
I am submitting herewith a dissertation written by Carley Elizabeth Harris entitled "An AutomatedDiagnostic Tool for Predicting Anatomical Response to Radiation Therapy." I have examined the finalelectronic copy of this dissertation for form and content and recommend that it be accepted in partialfulfillment of the requirements for the degree of Doctor of Philosophy, with a major in NuclearEngineering.
J. Wesley Hines, Major Professor
We have read this dissertation and recommend its acceptance:
Lawrence Townsend, Chris Pionke, Chester Ramsey, Laurence F. Miller
Accepted for the Council:Carolyn R. Hodges
Vice Provost and Dean of the Graduate School
(Original signatures are on file with official student records.)
To the Graduate Council: I am submitting herewith a dissertation written by Carley Elizabeth Harris entitled “An Automated Diagnostic Tool for Predicting Anatomical Response to Radiation Therapy.” I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Nuclear Engineering. J. Wesley Hines, Major Professor
We have read this dissertation and recommend its acceptance: Lawrence Townsend Chris Pionke Chester Ramsey Laurence F. Miller
Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of The Graduate School
AN AUTOMATED DIAGNOSTIC TOOL FOR PREDICTING
ANATOMICAL RESPONSE TO RADIATION THERAPY
A Dissertation
Presented for the
Doctor of Philosophy
Degree
The University of Tennessee, Knoxville
Carley Elizabeth Harris
December 2009
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ACKNOWLEDGEMENTS
I would like to acknowledge and thank those who helped me during my years of study in
Nuclear Engineering. I would first like to thank Dr. J. Wesley Hines for serving as my major
professor. His knowledge as a professor and guidance as a mentor have been greatly valued and I
am honored that I had the opportunity to finish my graduate study under his supervision. I also
would like to thank the other members of my committee, Dr. Chester Ramsey, Dr. Lawrence
Townsend, Dr. Christopher Pionke, and Dr. Laurence F. Miller, who have all provided me with
both personal and academic guidance through my years at the University of Tennessee,
Knoxville. They all are excellent professors and I am appreciative of their willingness to serve on
my committee.
I would also like to give a special acknowledgment to Dr. Alexander Usynin, who created
the tool that was used to collect all of my data. Also, I would like to thank him for all of the time
that we spent discussing my project and for sharing his useful knowledge about prediction
modeling.
I want to thank all of the professors and staff at the University of Tennessee who
continued to give me support through my years as a student. A special thank you goes to my
family in Estabrook, where Janet Coward, Dr. Roger Parsons, and Dr. Christopher Pionke greatly
contributed to both my personal and academic growth and allowed me the opportunity to teach
freshmen engineering students for four years.
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I would like to thank Dr. H. Lee Dodds for helping me find my way into the Nuclear
Engineering Department and for all of the encouraging words and guidance that I received from
him during my graduate years.
Finally, I want to thank my all of my family and friends for their continued
encouragement and support.
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ABSTRACT
A "Clinical Decision Support System" (CDSS) is a concept which has advanced rapidly
in health care over the last few decades, and it is defined as "an interactive computer program
that is designed to assist physicians and other health professionals with decision-making tasks."
Radiation therapy oncologists are required to make decisions that do not involve making a
disease diagnosis. Therefore this work focuses on developing a modified CDSS which can be
used to aid oncology staff in identifying cancer patients who will require adaptive radiation
therapy (ART).
An image-guided radiation therapy (IGRT) tool was developed that consists of both
diagnostic and prognostic processes. Patients who will require ART are those whose cross-
sectional neck measurements change by more than half of a centimeter over the entire course of
treatment. First, the tool allows one to “diagnose” or identify which patients would benefit from
adaptive therapy and then make a “prognosis,” or identify when ART is required.
Thirty head and neck (H&N) patients were used in this study, and 15 required ART.
Each diagnosis was made by predicting if the threshold of 0.5 cm would be crossed for each of
the four cross-sectional measurements, and each prediction was made by determining when the
threshold would cross. The diagnosis results show that half (61/120) of the measurements
predicted that patients would need ART given the first 15 observations and 28 of 120 predicted
needing ART within 20 observations. Therefore, 74% of patients' measurements accurately
diagnosed that ART would be required given just the first 20 observations. The prediction results
indicate that an average of 11 observations is needed to make adequate time predictions with a
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reliability of at least 0.5. However, more accurate time predictions with higher reliability values
(0.6 and 0.7) could be made given an average of 16 and 18 observations, respectively. These
predictions, while requiring more observations, provided additional lead time in knowing when
ART is required.
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TABLE OF CONTENTS
1 INTRODUCTION.......................................................................................................................1
1.1 BACKGROUND ...........................................................................................................1
1.2 ORIGINAL CONTRIBUTIONS ...................................................................................9
1.3 DOCUMENT ORGANIZATION ...............................................................................10
2 LITERATURE SURVEY .........................................................................................................11
2.1 INTRODUCTION .......................................................................................................11
2.2 ADVANCES IN RADIATION THERAPY ................................................................13
2.2.1 Portal Imaging .......................................................................................14
2.2.2 Image-Guided Radiation Therapy ..........................................................15
2.3 ADAPTIVE RADIATION THERAPY .......................................................................18
2.3.1 Identifying Uncertainty and Error ..........................................................20
2.3.2 Anatomical Changes ...............................................................................21
2.4 IGRT FOR ART IMPLEMENTATION ......................................................................24
2.5 CLOSING REMARKS ................................................................................................25
3 METHODOLOGY ...................................................................................................................27
3.1 DESCRIPTION OF THE DATA .................................................................................28
3.1.1 Data Collection .......................................................................................29
3.1.2 Reduction of Dimensionality ...................................................................34
3.2 DESCRIPTION OF THE DIAGNOSTIC PROCESS .................................................38
3.2.1 Tools for Diagnosis .................................................................................39
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3.2.2 Measure of Reliability .............................................................................45
3.3 DESCRIPTION OF THE PROGNOSTIC PROCESS ................................................46
3.3.1 Prediction Model Selection .....................................................................47
3.3.2 Reliability Metric ....................................................................................51
4 RESULTS………... ...................................................................................................................58
4.1 DIAGNOSIS ................................................................................................................58
4.1.1 Data Set Selection ...................................................................................58
4.1.2 Diagnosing ART Candidates...................................................................60
4.2 PREDICTION ..............................................................................................................71
4.2.1 Kernel Classification ..............................................................................75
4.2.2 Extrapolation Model ...............................................................................77
5 CONCLUSIONS .......................................................................................................................93
5.1 RECOMMENDATIONS FOR FUTURE WORK ......................................................94
REFERENCES….. .......................................................................................................................96
APPENDICES….. ......................................................................................................................102
VITA………………....................................................................................................................172
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LIST OF FIGURES
Figure 1 Representation of the need for adaptive radiation therapy
(H&N Patient 14) ................................................................................................................ 4
Figure 2 Patient who required adaptive therapy [Hansen 2006] .....................................................4
Figure 3 Target volume definitions ...............................................................................................12
Figure 4 Rapid anatomy changes for one H&N patient (a) before and
(b) three weeks into radiation therapy [Barker et. al 2004] ...............................................22
Figure 5 Cone-beam CT image showing anatomical response to radiation therapy [Dawson &
Sharpe 2006] ......................................................................................................................23
Figure 6 Patient 7 undergoing anatomical changes ......................................................................30
Figure 7 Cross-sectional measurement definitions .......................................................................32
Figure 8 Data collection tool .........................................................................................................32
Figure 9 Numerical example for calculating a trimmed mean ......................................................36
Figure 10 Trimmed Mean Performed ...........................................................................................37
Figure 11 Illustration of removing outlying data ..........................................................................37
Figure 12 Depiction of measurement “one” .................................................................................42 Figure 13 All measurements changed by 0.5 cm ..........................................................................43
Figure 14 No measurements cross the 0.5 cm threshold ...............................................................44
Figure 15 Illustration of calculating the Closeness to Crossover value ........................................56 Figure 16 Correlation vs. Closeness to Crossover ........................................................................57
Figure 17 Significant decreasing trend for Patient 14 ...................................................................62 Figure 18 Slightly decreasing trend for Patient 24 .......................................................................62
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Figure 19 Correlation vs. Percent Error for Case #1 .....................................................................73
Figure 20 Correlation vs. Percent Error for Case #2 .....................................................................74
Figure 21 Measurement 1 prediction after 10 observations ..........................................................86 Figure 22 Measurement 1 prediction after 15 observations ..........................................................87 Figure 23 Measurement 2 prediction after 10 observations ..........................................................87 Figure 24 Measurement 2 prediction after 15 observations ..........................................................88 Figure 25 Measurement 3 prediction after 10 observations ..........................................................88 Figure 26 Measurement 3 prediction after 15 observations ..........................................................89 Figure 27 Measurement 4 prediction after 10 observations ..........................................................89 Figure 28 Measurement 4 prediction after 15 observations ..........................................................90 Figure 29 Illustration of anatomical changes for Patient 17 after 25 treatments ..........................92
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LIST OF TABLES
Table 1 Patient demographic information .......................................................................................8
Table 2 Crossover data for Patients 1-10 ......................................................................................65
Table 3 Crossover data for Patients 11-20 ....................................................................................66
Table 4 Crossover data for Patients 21-30 ....................................................................................67
Table 5 Diagnosis of ART candidates...........................................................................................69
Table 6 Kernel Classification prediction results ...........................................................................75
Table 7 Reliability for patients requiring ART .............................................................................78
Table 8 Prediction results for a reliability > 0.5 ............................................................................82 Table 9 Prediction results for a reliability > 0.6 ............................................................................83 Table 10 Prediction results for a reliability > 0.7 ..........................................................................84
1
1 INTRODUCTION
The primary goal of radiotherapy is to destroy tumor cells contained in a volume. This
volume is physician-defined and is often times located nearby or in normal, healthy tissue in the
body. There becomes a certain compromise or conflicting goal when treating with radiotherapy,
because it is desired to use enough radiation to treat the pre-defined target volume while still
sparing the function of surrounding normal tissues and structures. In order to find the best
compromise and have a successful outcome of the primary goal, both accurate and reproducible
radiation treatments must be delivered over the entire course of therapy.
External-beam radiation therapy (EBRT) involves treating cancer patients with radiation
which leaves the source (linear accelerator) and enters through the skin. It has been in use for the
last 100 years and in the last eight decades it has proven to be an effective therapeutic means for
controlling and even curing cancer. In the last fifty years, rapid technological advances have
allowed for the progression of exceptional radiation therapy treatment for all types of cancer
patients.
1.1 BACKGROUND AND RESEARCH NEED
Head and neck (H&N) cancer is one type of cancer that can be a challenge to treat. There
are a few options for treatment, including radiation therapy, chemotherapy, surgery, or a
combination of these. When using radiation therapy, H&N cancer can be difficult to treat in
terms of covering the tumor adequately, avoiding normal tissues, and delivering the radiation to a
precise location in the head and neck region. As mentioned before, the goal of radiation therapy
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is to deliver an accurate and reproducible treatment. To be accurate, normal structures, such as
saliva-producing parotid glands, spinal cord, and optic nerves, must be avoided, and
reproducibility is achieved with the use of immobilization devices.
H&N patients are of particular interest in radiation therapy because they are known to
incur large anatomical changes (weight loss, tumor shrinkage) over the course of treatment. If
large enough changes occur, the immobilization device, or facemask, can become ineffective in
holding a patient stationary and treatment plans can become invalid due to the delivery of
inaccurate doses. Once the accuracy and reproducibility of a plan is compromised, there is need
for an adjustment before the completion of a patient’s treatment. Therefore, the two main
concerns for head and neck patients are that the facemask is correctly immobilizing the patient's
head during treatment and that the correct radiation dose is being delivered. If either one of these
is compromised, then there becomes a need for adaptive therapy. One might ask, "How do we
know which patients are incurring large anatomical changes?" or "At what point during the
course of treatment should we be concerned about delivering an incorrect radiation dose due to
an ineffective facemask?" Answering these questions ultimately identifies those head and neck
patients who would "benefit from adaptive radiation therapy," and finding the answers is the
main focus of this dissertation.
The main problem is quantifying “significant anatomical change” and determining which
patients have undergone enough change to require a new facemask and/or a new treatment plan.
While the main focus of this dissertation is to identify those patients who will need a new
facemask, it is still important to note the consequences of not having a new mask, meaning the
incorrect delivery of radiation from an invalid treatment plan. Without adapting a treatment plan,
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it is possible for a patient to experience increased doses to normal tissue while under-dosing the
tumor volume. This is extremely undesirable due to a chance of tumor recurrence or toxicity to
the patient.
Figures 1 and 2 illustrate exactly why there is a need for adaptive radiation therapy. The
first patient experienced such significant tumor response to treatment that the mask no longer
touched the sides of the face, which should have required a new mask to be made. However, no
new mask was made and the reason for this is unknown. The second patient experienced changes
that caused the doses to shift, risking over-irradiating the spinal cord, and the middle image
shows what would have been delivered if no adaptive therapy had taken place. However, a new
treatment plan was developed and the last image shows the corrected plan which is no longer
directly delivering dose to the patient's spinal cord.
Many times in a real clinic setting, the oncologist is unaware of significant changes such
as those shown for these patients. Therefore a method should be available for when to implement
adaptive therapy, where patients requiring additional attention could be identified and the
physician could proceed with the appropriate line of action. Methods are currently available for
making decisions regarding a diagnosis, but cancer patients have already been diagnosed and
often require a different type of decision-making during their course of treatment.
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Figure 1: Representation of the need for adaptive radiation therapy (H&N Patient 14)
Figure 2: Patient who required adaptive therapy [Hansen 2006]
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A "Clinical Decision Support System" (CDSS) is a concept which has advanced rapidly
in health care over the last few decades. The idea of having computer-aided diagnostic tools
began back in the 1970's [de Dombal et al. 1972], and a CDSS is defined as "an interactive
computer program that is designed to assist physicians and other health professionals with
decision-making tasks." More recently, a CDSS could be knowledge-based or non-knowledge-
based, and the methodologies used by the systems are already well-known for their problem-
solving abilities. Examples of these methods include Bayesian Networks, Neural Networks,
Genetic Algorithms, Rule-Based Systems, Logical Condition, and Causal Probabilistic
Networks. All methods are useful in the development of a CDSS, and much research has been
done on the feasibility and effectiveness of using the system to make a decision or diagnosis
[Johnston et al. 1994]. While the popularity of these systems is growing for many general
practicing physicians, radiation oncologists are required to make slightly different types of
decisions which do not involve making a disease diagnosis. Therefore, a modified CDSS, such as
the IGRT tool presented in this work, can be used to aid oncology staff in decision-making
processes.
The idea of compensating for changes and customizing treatment plans to individual
cancer patients was first introduced by members of the Department of Radiation Oncology at the
William Beaumont Hospital [Yan et al. 1997a]. Termed adaptive radiation therapy, the idea was
proposed as a closed-loop radiation treatment process where treatment plans could be modified
by using a systematic feedback of measurements. The first use of this process was measuring and
correcting for treatment position variation, with mention of eventually extending it to the
measurement of target geometric variation, or tumor changes.
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Many aspects of radiation therapy have to be monitored and checked during the course of
treatment, including patient positioning, doses delivered to the tumor and surrounding tissue, and
normal/tumor tissue response to radiation. After the concept of ART was introduced, many
studies involving imaging and adaptive therapy followed, attempting to address all of these
aspects. For example, Yan et al. [1998] conducted an ART feasibility study using portal imaging
to evaluate patients for setup error due to incorrect patient positioning, finding that ART was
extremely valuable in routine clinical practice for reducing systematic errors. Hansen et al.
[2006] evaluated the dosimetric effects on target volumes and normal tissues by repeating and
evaluating daily CT scans. Work done by Barker et al. [2004] involved quantifying volumetric
and geometric tumor changes that occurred for H&N patients during courses of radiotherapy.
These three studies have been extensively referenced in other publications whenever adaptive
therapy is mentioned. They all conclude that using available imaging technology along with
ART is extremely valuable and that systematic measurements and data can be useful in the
development of an ART scheme.
True implementation of an ART process will require a new infrastructure where the
clinical procedures such as treatment planning, treatment verification, treatment evaluation, and
treatment adjustment are not performed as independent tasks. This dissertation will concentrate
on the treatment evaluation and adjustment parts of an ART process by developing an off-line
image-guided radiation therapy tool. This tool will allow for H&N patient measurement data to
be evaluated and provide feedback to the radiation oncologists so that any necessary adjustments
can be made. In addition to describing the model used to build the tool, this dissertation will
explain how the tool can be integrated into currently-established IGRT systems so that
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measurements can be automatically made and analyzed while the patient is undergoing
treatment.
Table 1 shows a description of the 30 patients used to develop the IGRT tool. The
demographic information is shown to point out that several types of diseases and staging were
used for the study, and that an effective ART method should accommodate any type of H&N
patient. Other demographic information, such as whether or not a patient was receiving
chemotherapy during radiation, surgery status, as well as total prescribed dose were also
considered important information to report.
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Table 1: Patient demographic information
Patient Number
Primary Site Stage Pathology Chemo-therapy
Surgery Dose (Gy)
1 Unknown TXN2M0 Squamous Concurrent Post-Op 66.0 2 Unknown TXN1M0 Squamous Concurrent None 70.2 3 Tonsil T2N1M0 Squamous Concurrent None 61.2 4 BOT* rTXNXM0 Squamous Concurrent None 68.0 5 Larynx T3N0M0 Squamous Concurrent None 66.4 6 Epiglottis T2N0M0 Squamous Concurrent None 66.0 7 BOT T1N1M0 Squamous Concurrent Post-Op 66.0 8 BOT T1N1M0 Squamous None Post-Op 60.0 9 Tongue T2N1M1 Squamous Concurrent None 68.4
10 Pharynx T2N1M0 Squamous Concurrent None 62.1 11 Supraglottic Larynx T2N2bM0 Squamous Concurrent None 64.0 12 BOT rT3N3M0 Squamous Concurrent None 68.5 13 Pyriform Sinus T4N1M0 Squamous Concurrent None 70.2 14 Epiglottis TXN2bM0 Squamous Concurrent None 50.0 15 Tonsil T2N2M0 Squamous None Post-Op 61.2 16 Unknown TXN1M0 Squamous Concurrent Post-Op 64.8 17 Nasopharynx T3N0M0 Squamous Concurrent None 70.2 18 Larynx rTXN2M0 Squamous Concurrent None 50.0 19 BOT T4N2M0 Squamous Concurrent None 67.6 20 Retromolar Trigone T2N1M0 Squamous Concurrent Post-Op 52.2 21 Oral Tongue T2N0M0 Squamous None Post-Op 63.0 22 Supraglottic Larynx T2N0M0 Squamous None None 70.0 23 BOT T1N2aM0 Squamous Concurrent None 52.0 24 Unknown TXN1M0 Squamous Concurrent None 63.0 25 Nasopharynx TXN0M0 Adenoid Systic None Post-Op 61.2 26 Retromolar Trigone T2N0M0 Squamous None None 70.0 27 BOT T1N2aM0 Squamous Concurrent None 70.4 28 Tonsil T2N1M0 Squamous Concurrent Post-Op 63.0 29 Nasopharynx T2N1M0 Squamous Concurrent Post-Op 70.4 30 Pharynx T2N0M0 Squamous Concurrent None 59.4 *BOT = BASE OF TONGUE
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1.2 ORIGINAL CONTRIBUTIONS
Many studies published in the literature in the last ten years have identified the problems
associated with anatomical changes in H&N patients by analyzing tumor volume regression,
parotid shift data, parotid volume loss, and dose reconstruction data; but none have determined
which specific patients should (and when) have their plans adapted. In this study, a new type of
data will be analyzed with an approach that has never been used before in order to determine
which patients would "benefit from ART." An outline of the original contributions of this
dissertation is presented below.
1. Development of a method for identifying which head & neck patients would most
benefit from adaptive radiation therapy, with a focus on those patients who will
require a new facemask. The method employs 4 different cross-sectional
measurements, taken from the neck region, and evaluates the change in measurement
for each.
2. Development of a reliability metric for the H&N patients identified as needing
adaptive radiation therapy.
3. Development of a method for predicting when H&N patients will need ART.
4. A metric for reporting reliability when making a prediction, which will aid the
oncologist in making a decision. The reliability metric takes into account how well
measurements are correlated along with a “closeness to crossover” parameter, which
is a measure of how close (in time), a patient’s measurements are to crossing a
particular threshold.
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5. An original automated Image-Guided Radiation Therapy tool to analyze the collected
head & neck measurement data. The two-part IGRT tool will aid oncologists and
physicists in the clinical setting by alerting them if and when patients will require
adaptive radiation therapy during the course of their treatment. With the ability to
make an accurate prediction, time and resources can be pre-allocated to take into
account the extra work load from having to create new treatment plans and/or a new
facemask.
1.3 DOCUMENT ORGANIZATION
A detailed literature survey of current radiotherapy practices, the advent of image-guided
radiation therapy, and the use of new treatment techniques to implement adaptive radiation
therapy is presented in section 2. Next, the methodology for developing the IGRT tool will be
discussed in section 3. Then in section 4, the results of using this tool as an integrated decision
making process are presented. Finally, section 5 concludes the dissertation and provides
recommendations for future work.
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2 LITERATURE SURVEY
This section provides a literature survey of the need and benefits for treating head and
neck cancer patients with adaptive radiation therapy. The survey will give an overview of current
radiation therapy practice, the recent advances in the field of radiation oncology, methods used
for managing uncertainty associated with treating head and neck patients with radiation therapy,
and how using IGRT can be implemented into the ART process.
2.1 INTRODUCTION The current clinical process for developing a radiation treatment “plan” involves first
acquiring an image set of the patient. The majority of patients simply undergo a pre-planning
computerized tomography (CT) scan, while some patients require additional diagnostic scans,
such as a PET scan or an MRI series to better identify the tumor site. The images acquired are
crucial in determining where radiation is to be delivered.
Once diagnostic and planning images are obtained, a computerized treatment planning
system (TPS) is utilized for contouring and calculating doses for any given volume within the
patient. The International Commission of Radiological Units (ICRU) defines the gross tumor
volume (GTV) as “gross palpable or visible/demonstrable extent and location of malignant
growth.” The clinical target volume (CTV) includes gross tumor volume “and/or sub-clinical
microscopic malignant disease which have to be eliminated.” It is the CTV that we hope to
eradicate by the application of radiotherapy. [ICRU Report #50 1978].
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Another important volume used in radiation therapy, also introduced by the ICRU in
1978, is the primary target volume (PTV), and it is typically the clinical treatment site or target
for which a prescription of radiation dose is to be delivered. It is comprised of the CTV plus an
appropriate margin, the extent of which is determined by the physician. The PTV was defined
due to the recognition that there are treatment uncertainties, such as organ motion and/or patient
positioning errors. At the end of the planning process, the product is a treatment plan with
specifications for total radiation dose, daily treatment doses (called “fractions”), field sizes, and
field shapes. Figure 3 illustrates how the target volumes are defined by the ICRU.
Figure 3: Target Volume Definitions
13
2.2 ADVANCES IN RADIATION THERAPY Oncologists and physicists have always been concerned with delivering the best radiation
treatment possible to cancer patients without damaging normal, healthy tissue. An article by
Herring and Compton [1970] explains the importance of disease control and normal tissue
complication rates with respect to dose. Based on their evaluation, they recommend that the dose
delivered over the course of treatment should be within ±5% in order to control local disease and
spare normal tissue function. In order to stay within the 5%, accuracy and precision would be
required at each step of the treatment process, and therefore some kind of system would need to
be implemented. This system would take into consideration both dosimetric and geometric
factors, which can adversely affect the quality and accuracy of radiation therapy treatments.
According to Jaffray, Yan, and Wong [1999], the required geometric precision of radiotherapy
treatments must be selected based on treatment site and cost. They explain that on-line
techniques (where the patient is on the treatment table during measurement) are best suited for
identifying intra-fraction setup errors and organ motion during treatment, while off-line
strategies (done outside of patient treatments) best identify inter-fraction (daily) errors.
The ICRU began providing reports for recommendations and standards of radiation in the
1950’s, and the information in ICRU report #50 [1978] has set a standard for the practices in
radiation oncology. As mentioned before, the report defines such things as the CTV: the region
which contains the gross tumor volume (GTV) and then a margin around for microscopic
disease. The planning target volume (PTV) includes the CTV as well as margins to account for
patient setup and organ motion.
14
With a set of suggested limits and standards in place, oncologists and physicists
continued to use the available tools for creating and delivering acceptable treatment plans. The
next sections will describe the advancement of those tools, specifically the use of imaging for
treatment verification, and how they have allowed for the advancement of adaptive therapy.
2.2.1 Portal Imaging
Early studies regarding geometric uncertainty in EBRT used portal films to evaluate
setup errors during treatments. A study done by Marks et al. [1974] evaluated 99 lymphoma
patients and detected 330 localization errors (out of 902 films) by using treatment verification
films, or portal films. Their study concluded that “treatment verification films are invaluable in
the detection of localization errors in the treatment of lymphoma, and that they can lead to a
reduction in localization errors if they are used regularly.” Similar studies were done during the
70’s and, due to the realization that verification films were very useful in detecting errors, the
development of electronic portal imaging devices, or EPIDs, began. A pilot study done by Boyer
et al. [1992] looked at the limitations of imaging with high-energy radiation beams as well as the
different techniques used to produce EPIDs. An extensive review of optical systems using direct
x-ray-induced fluorescence was done, and they concluded that EPIDs provided images
comparable to radiographic film (which was the standard until the early 90’s) and were
convenient due to the ability to store and transfer them electronically.
Because the goal of radiotherapy is to treat cancerous tumor cells within the CTV while
sparing normal surrounding tissues, it is necessary that patient setup errors be quantified and
reduced. The early use of EPIDs allowed for the identification and measurement of these errors.
15
Work done by Erridge et al. [2003] examined patient set-up, tumor movement, and tumor
shrinkage for lung cancer patients. They produced results which validated the use of EPIDs to
reduce set-up error and to localize the tumor more accurately than with radiographic film alone.
Hurkmans et al. [2001] reviewed clinical practices performed at The Netherlands Cancer
Institute and developed a set of recommended allowable set-up deviations for different cancer
sites, including H&N, prostate, pelvis, lung, and breast. These recommendations were a result of
using EPID images to come up with criteria for “good clinical practice” in patient positioning.
2.2.2 Image-Guided Radiation Therapy
The use of 3D imaging in radiation oncology has become routine in clinical practice. In
the treatment planning process, images of the patient are used by the oncologists to identify the
tumor location as well as where the relative normal tissues are located. The frequent use of
imaging in the treatment room during a course of radiation therapy, where decisions are made
based on these images, is called image-guided radiation therapy, or IGRT. Jaffray explains that
this type of volumetric imaging permits “unprecedented characterization of the diseased and
normal structures with precise and accurate determination of geometric location” [2005].
Dawson and Sharpe [2006] reviewed IGRT in terms rationale, benefits, and limitations.
They determined that the rationale for IGRT was to verify and guide treatments, which is
important for identifying setup errors and daily tumor changes. The benefits of IGRT are the
ability to measure tumor changes and reduce setup error after identification of the error. And, the
major benefit is the ability to verify increased doses to tumors. Without the prior ability to verify
dose distributions, tumors were treated at lower doses so as to not over-dose normal tissue. But
16
with the advent of IGRT, higher, monitored doses can be delivered to the patient and local
disease control and survival rates have increased. The only evident limitation is that no one
technology or strategy exists that is appropriate for all clinical situations. But, IGRT does
provide the opportunity to adapt radiotherapy to anatomical changes that could not be done
previously.
Sorcini and Tilikidis [2006] reported on the clinical application of IGRT using a Varian
linear accelerator with an on-board imager (OBI). Their study, conducted in Stockholm, Sweden,
evaluated using the OBI for on-line set-up correction of prostate patients using internal gold
markers. They measured the displacements of the markers and were able to accurately track the
position of patients’ internal anatomy.
The usefulness of IGRT has been extensively researched and is present in most clinical
treatment facilities in the world. IGRT allows for daily identification of the size, shape, and
location of the tumor so that positional adjustments can be made if needed. This advantage
permits better tumor control and reduces the risk of toxicity after radiotherapy is complete.
Computed Tomography (CT)
Originally, a single set of CT scans was acquired prior to the start of a patient’s treatment
and used as a basis for radiotherapy planning. Soon after it was realized that the position of the
tumor could change from fraction to fraction, routine CT-imaging prior to each daily treatment
was adopted. The study done by Barker et al. [2004] evaluated the use of a CT/linear accelerator
combination as a means for measuring anatomic changes during H&N radiotherapy. The results
17
from 14 H&N patients showed that significant volumetric and positional changes occurred for
the gross disease. By acquiring daily pre-treatment CT scans, it is possible to monitor tumor
position and help reduce uncertainty in the treatment.
Newer types of CT scans are currently available, such as megavoltage CT (MVCT) and
megavoltage cone beam CT (MVCBCT) which allow for better contrast in the images. And, the
use of integrated CT/linear accelerators is becoming more widely accepted. Helical
TomoTherapy machines include an on-board MVCT scanner to acquire images just prior to
treatment. Elekta’s Synergy is available with an on-board MVCBCT scanner. Megavoltage cone-
beam CT is a technique used to acquire images by using a cone shaped x-ray beam rather than a
conventional linear fan beam, projecting it through the patient, and using a detector on the other
side to record the attenuation of the x-rays through the patient. In contrast, MVCT images, such
as those acquired on a TomoTherapy machine, are made by detecting a fan beam of x-rays
projected through the patient.
Pouliot et al. [2005] conducted a feasibility study of MVCBCT with respect to
acquisition time and quality of images. Because of the newness of the technology, both frozen
sheep and pig heads were scanned and images were reconstructed to evaluate the quality of bony
anatomy identification. It was concluded that MVCBCT produced excellent images; however
surface dose to the skin (2 cGy/film) could prevent acquiring the images every day. Oldham et
al. [2005] evaluated an on-line application for MVCBCT and found that the images produced by
this technique provide for excellent delineation between soft tissue and bone, allowing for
correct setup verification.
18
Dawson and Jaffray recently conducted a review of the advances in IGRT and how it can
be extended even further beyond its current uses. With the realization of the clinical benefit that
IGRT has for verifying correct patient positioning and monitoring day-to-day anatomical
changes, studies are now moving forward to not just identify problems, but correct them as well.
Looking on to future applications of IGRT, “Adaptive radiation therapy can now be exploited,
and the impact on replanning or changing the treatment course on the basis of information
obtained during treatment can be investigated” [2007].
2.3 ADAPTIVE RADIATION THERAPY
As mentioned previously, members of the William Beaumont Hospital introduced the
term adaptive radiation therapy as “a radiation treatment process where the treatment plan can be
modified using a systematic feedback of measurements” [1997]. The idea is simply aimed at
improving radiation treatments by monitoring daily changes and integrating the information into
a new, re-optimized plan, which is customized for each patient.
The first studies done using ART involved taking portal films and making set-up
adjustments at that time [Bel et al. 1995; Michalski et al. 1996]. This approach is termed on-line
ART because the adaptation is done while the patient is lying on the table and used to make
decisions regarding shifting a patient’s position on the treatment table. A newer approach, off-
line ART, is becoming more popular and it is believed to be better in terms of correcting for
systematic and random errors [Hong et al. 2005]. Off-line adaptive therapy involves using data
which has previously been collected and then evaluating it later in order to make a decision.
19
Currently, there is sufficient literature to support the idea of adapting plans during the
course of treatment for cancer patients. Brabbins et al. [2005] conducted a dose-escalation trial
on prostate patients and determined that using adaptive therapy to locate the internal location of
the tumor, along with radiotherapy, allowed for reduced toxicities after the completion of
treatment. Yan et al. [1997b] used adaptive modification of treatment planning to minimize the
effects of set-up error, and work done by Sharpe et al. showed that adaptive planning and
delivery is extremely effective in accounting for anatomical changes induced by radiation
therapy [2005]. One specific new application involves tracking lung motion and lung tumors
within the lungs. Using adaptive therapy along with image-guided TomoTherapy treatments has
been studied for the last few years [Ramsey et al. 2004, 2006], and new techniques have been
developed which have lead to the better treatment of lung cancer patients.
Only a few recent studies have addressed the issue of ART for those patients
specifically with H&N cancer. There have been studies which identify the dosimetric effects due
to not replanning. For example, Jenkins et al. used daily MVCT imaging to determine dose
variations for H&N patients. They found that it is necessary to perform daily imaging so as to not
incorrectly treat patients [2005]. Doemer et al. came to a similar conclusion after analyzing 15
H&N patients and determining that adapting patient plans was frequently necessary due to
increasing doses to the spinal cord [2008]. Other studies, such as the one done by Wu et al.
[2007] looked at adaptive replanning strategies accounting for tumor and normal tissue shrinkage
for patients undergoing treatment. These studies emphasize how essential it is to evaluate both
tumor volume response and dose distributions when treating H&N areas.
20
In the literature, there have been no new approaches for identifying which patients would
benefit from ART and then when exactly those plans should be adapted during the course of
radiotherapy treatment. The first step in ART is identifying the places for possible uncertainty
and error in a patient’s radiotherapy treatment.
2.3.1 Identifying Uncertainty and Error
A review was conducted of the advances in identifying uncertainty [Jaffray et al. 1999].
From this reviewing of the current literature, it was found that uncertainty typically is
characterized as either setup errors or organ motion. Setup errors are a result of variations in the
positioning of the patient’s bony anatomy, while organ motion is variation in the target (tumor)
within the patient. It was concluded that the magnitude of organ motion errors were significantly
less than setup errors and accommodations in planning procedures should be made.
As mentioned before, the study done by Hurkmans et al. [2001] did an extensive
evaluation of set-up verification using portal imaging in their clinic. The study separated setup
errors into two main classes: random and systematic. Random errors are inter-fraction errors
which are deviations between fractions, and systematic errors are deviations between the planned
patient position and the average patient position over a course of fractionated therapy.
Being able to identify potential errors allows for correction before a patient is treated
incorrectly. As such, daily setup variation should be incorporated into plans in order to achieve
the best treatment. Studies have been done on predicting systematic and random errors [Zeidan et
al. 2007; Allen et al. 2007] and how they can be used in conjunction with the ART process. But,
21
while evaluating setup error is extremely important, other sources of error should not be
overlooked when using an adaptive approach to radiation therapy.
2.3.2 Anatomical Changes
During a radiotherapy course, many H&N patients experience substantial anatomical
changes due tumor response, nodal mass response, edema, or overall weight loss. Wang [2007]
looked at how body weight loss is associated with error in treating nasopharyngeal patients.
Barker [2003] measured tumor volumes at the beginning and end of treatments, finding that the
response to radiation can cause drastic changes in volume over the course of therapy. Meldolesi
[2006] examined skin volume reduction and parotid size change in nine H&N patients. Lee
[2007] measured how the parotid glands physically moved with respect to the tumor. All four
studies measured and identified particular anatomical changes that can occur over the course of
radiotherapy treatments, which should all be taken into consideration by the radiation therapy
staff.
These changes in anatomy can prove to be a problem if they affect the quality of a
patient’s radiation treatment. A few studies have identified H&N patients as potential ART
candidates due to their response to radiation treatment [Feng & Eisbruch 2007; Meyer et al.
2007], while several studies have evaluated tumor response by looking at images taken from
various IGRT devices. Because immobilization devices, such as masks and bite-blocks placed in
the mouth, are typically used to hold the patient in place during treatment, losing weight or a
reduced tumor mass can take away from the effectiveness of the device. As such, it might be
necessary to adapt the patient’s treatment plan to account for these specific anatomical changes.
22
Tumor Response to Radiation
Barker et al. [2004], as previously mentioned, conducted a retrospective study to
determine the dosimetric effects on normal tissue and tumor volumes using daily CT scans and
replanning during the course of IMRT. The study reported that the gross tumor volume
decreased throughout the course of radiotherapy at a median rate of 1.7% - 1.8% per day, and the
volume loss was frequently asymmetric. Figure 4 shows an example of how rapid anatomy can
change (especially the tumor) during radiation therapy. The left panel depicts the CT image prior
to treatment, and the right panel shows the same image after the patient had undergone 3 weeks
of treatment. Changes such as this would most likely require the patient to have their plan
adapted during the course of the typical (6-7) week treatment period.
Figure 4: Rapid anatomy changes for one H&N patient (a) before and (b) three weeks into
radiation therapy [Barker et. al 2004]
(b) (a) (b)
23
Weight Loss
H&N patients can also experience significant weight loss during their course of
radiotherapy. This can be due to tumor shrinkage or overall weight loss. This effect, along with
tumor response, could potentially result in an under-dosage of the tumor/target tissue or an over-
dosage to normal tissues. The same study done by Barker with regard to tumor response also
involved evaluating dosimetric consequences due to weight loss. The study reported a median
weight change from start to finish of treatment as -7.1%. Therefore, it is evident that weight loss
plays a significant role in determining if a patient will require ART. A review of IGRT in terms
of its rationale, benefits, and limitations was previously mentioned [Dawson & Sharpe 2006].
Figure 5 shows two cone-beam CT images taken of the same patient at the beginning of
treatment (A) and then after 3 weeks (B) of treatment, and anatomical change (weight loss) can
clearly be seen when using this imaging modality.
Figure 5: Cone-beam CT image showing anatomical response to radiation therapy [Dawson &
Sharpe 2006]
24
2.4 IGRT FOR ART IMPLEMENTATION
The use of daily imaging has been proven to aid oncologists and therapists is identifying
when changes have occurred during the course of therapy. With these daily images, it is possible
to conduct retrospective analysis for any given patient. At the Thompson Cancer Survival Center
in Knoxville, TN, a Helical TomoTherapy machine is used daily to treat the majority of H&N
patients who are undergoing external beam radiation therapy. This machine includes an on-board
CT scanner which is used to acquire daily pre-treatment images for the purpose of verifying the
correct set-up of the patient. When a patient is finished with their treatment, their information is
archived in a database.
The most significant ART studies done using IGRT were done by Barker [2004] and
Hansen [2006]. Both authors extensively evaluated volumetric, geometric, and dosimetric effects
on H&N patients without replanning their treatments. In 2004 Barker used “high-definition
imaging studies throughout the treatment course to quantify the magnitudes and rates of three-
dimensional anatomic changes occurring gradually over time.” It was concluded that GTV’s
typically regress asymmetrically and that the results might have potential dosimetric implications
when conformal treatments are used. In 2006, Hansen and his co-authors followed up that study
by performing repeat CT scanning to evaluate doses to target volumes and normal tissues. They
reported not being able to separate the effects of tumor shrinkage and weight loss due to the
small sample size (n=5) of patients. And, that “future studies should investigate the effects of
asymmetric location of normal structures and/or target volumes, asymmetric volume losses, and
the use of multiple asymmetric beam angles on the dosimetry of target volumes and normal
25
tissues.” The results from these two studies suggest the great need for a tool to aid clinicians in
determining which patients should have their plans adapted.
It is known that imaging in the treatment room has provided extensive data which
documents movement and anatomic change of targets during and between treatment fractions.
Many investigators demonstrated this in their studies by using 3D images to obtain tumor
response measurement data [Kupelian et al. 2005; Sohaib et al. 2000; Siker et al. 2006]. This
data can be incorporated with the various anatomical changes in order to manage target
movement and setup uncertainty. For example, a few studies have reported using daily CT
images to aid in adaptive procedures [Liang et al. 2008; Lu et al. 2006]. The only question that
has yet to be answered is how exactly to implement the data (obtained from IGRT devices) into a
decision making process.
2.5 CLOSING REMARKS
Most studies that have involved measuring initial tumor volume propose a “percent loss
threshold” approach for future studies in order to make an ART decision. For example, one
recent study suggested that if the tumor volume is reduced by 30%, then a new plan should be
created [Woodford et al. 2007]. This is a valid approach, but patients can lose body weight
without losing tumor volume.
For instance, if a patient were prescribed 1.8 cGy in 35 fractions but then lost a
significant amount of weight in the first 15 fractions, it would be of interest to the oncologist to
as to how this would affect the outcome of the remaining 20 fractions of the radiation treatment
plan. If one were to use the proposed 30% tumor reduction threshold but the patient lost enough
26
weight in the first 15 fractions to cause the parotid glands to move into the PTV and the tumor
never reached that threshold, one could potentially damage the patient’s salivary function.
As stated before, the best way to have an overall successful outcome of treatment, both
accuracy and reproducibility must exist in radiotherapy. When a patient loses weight or
experiences anatomical change to radiation, these two principles can become compromised.
Accuracy is lost if a tumor is no longer receiving the prescribed dose (thereby under-dosing) or if
normal structures are receiving too much dose. Reproducibility is missing if the immobilization
device is no longer effective.
Therefore, if a patient loses weight in the neck and an immobilization device is no longer
effective, then they should have their treatment course adapted, and more than likely will require
construction of a new facemask. By using a percent threshold, a patient may be overlooked when
adapting their treatment plan would be the more appropriate line of action. By using cross-
sectional measurement data to evaluate changes in daily measurements, weight loss patients and
tumor response patients can be distinguished and a more informed decision can be made about
which patients (and when) would "benefit from ART." More specifically, this dissertation
focuses on developing a modified clinical decision support system to aid clinical staff in decision
making-processes.
27
3 METHODOLOGY
This section will describe the basic methodology for developing the IGRT tool, which is
the major contribution of this dissertation. The main purpose of this tool is to help in answering
two questions. The first is, “How do we know which patients will benefit from adaptive radiation
therapy?” The second is, "If a patient will require adaptive therapy, at what point in their
treatment should an adaptive approach be implemented?" In other words, "How do we know
which patients are incurring large anatomical changes, be it weight loss or tumor response?" and
"At what point during the course of treatment should we be concerned about delivering an
incorrect radiation dose due to an ineffective facemask and/or treatment plan?"
The solution to answering these questions presents itself twofold: solving a diagnostic
problem in identifying “who,” and then dealing with a prognostic-type problem to predict
“when.” Answering these questions ultimately identifies those head and neck patients who would
benefit from adaptive radiation therapy, and finding the answers is the main focus of this
dissertation. Once the answers are known, the ultimate result is either:
1. The patient is not experiencing enough anatomical response to radiation treatment to
require adaptive therapy.
2. The patient is experiencing significant anatomical response to radiation and will
require adaptive therapy. If so, then:
a. The patient needs the creation of a new treatment plan but no new facemask.
b. The patient needs the construction of new facemask but no new treatment
plan.
28
c. The patient needs both a new facemask and a new treatment plan.
To help in answering these questions, cross-sectional measurement data taken from thirty
patients’ neck regions were evaluated based on the amount of change in the first few treatment
fractions. By identifying those patients whose measurements exhibit early "significant change," a
decision can be made as to whether or not a new mask should be made and/or if a new treatment
plan should be developed. If the answer to the first question is "yes", then the second question
will be answered by predicting at what point in time adaptive therapy is required. If the answer is
“no” then it is assumed that the patient should require no re-evaluation.
In this dissertation “significant change," according to the physician at the institution
where the data was collected, is a change greater than 0.5 cm in any of the four cross-sectional
measurements. The patients who exhibit this magnitude of change would potentially require
ART, and these are the patients who are the most important to identify and correct for at the
appropriate time. A description of how the data was acquired and then how it was used to
develop a diagnostic method for answering the first question will be presented, along with the
techniques used to make a time prediction, thereby answering the second question.
3.1 DESCRIPTION OF THE DATA
It is known that human tissue responds to radiation: large tumors tend to significantly
decrease in size over the course of treatment, and patients tend to lose weight due to side effects
of radiation and/or chemotherapy (nausea, dysphasia, etc.). Because H&N patients often
29
experience gradual changes in their anatomy during radiotherapy, daily CT scans are very useful
in identifying those changes. For the TomoTherapy machine, once a patient is scanned, the
original planning CT image is fused along with the new scan. By looking at the superimposed
images, significant changes in anatomy can easily be identified and measured.
3.1.1 Data Collection
Figure 6 shows an example of a patient who experienced "significant" anatomical
changes, and for this case, it was attributed to overall weight loss. The gray image is the original
planning image, which was acquired before the patient began treatment. The blue image was
taken in the 5th week of treatment. It is apparent that the patient had an overall reduction in the
neck region and perhaps more on the right side near the C2-C3 level.
Previously discussed studies have evaluated data from volumetric changes in tumors and
normal tissues. The results from the Hansen study show that no distinction could be made
between the significance of tumor response and weight loss. However, by taking cross-sectional
measurements of the head and neck region and looking at the rate of change in the first few
treatments, it appears as though this distinction could be made. For example, if measurements
taken on the right side of the face change at a faster rate than those on the left side, then one
could say that patient was experiencing tumor response to radiation. However, if all
measurements around the head and neck region are changing rapidly, one might say that patient
is simply losing weight.
30
Figure 6: Patient 7 undergoing anatomical changes
Data Collection Tool
A total of 1450 MVCT images were obtained from the TomoTherapy archive database
and evaluated for tumor response, weight loss, and anatomical changes. Cross-sectional head and
neck data was collected for 30 H&N patients for a total of over 30,000 measurements. These
measurements were taken using each patient’s daily pre-treatment MVCT scans and included
anterior-posterior (AP) and lateral (LAT) measurements. The AP measurement is beneficial
when evaluating incorrect positioning; however it was used here only for the purpose of locating
the midpoint of the patient’s head. Therefore, two lateral measurements were taken with respect
to the center of each vertebral body, and two other lateral measurements were taken with respect
to the midline of the patient’s head, which totals 4 lateral measurements for each available
vertebra (maximum of 5). Figure 7 shows how these measurements were taken with respect to
31
the center of the vertebral bodies. Starting with the red solid line in the figure and moving
clockwise around the head, we have measurements one through four, respectively.
A graphical user interface (GUI) was developed by Dr. Alexander Usynin at the
Thompson Cancer Survival Center in Knoxville, TN for the purpose of taking these cross-
sectional measurements in a consistent and efficient manner. The tool was created in MATLAB
and was used to recall archived imaging data, (from the TomoTherapy database) which allowed
for the collection of the cross-sectional measurement data. Figure 8 shows an example of this
tool, and it was used to collect all data from the 30 H&N patients. Once all daily MVCT images
are loaded, the user has the ability to scroll through the kVCT (original) scan until the desired
slice if found. Then, the user “Accepts Slice” and the corresponding MVCT image appears in the
window, and now has the ability to pick any point on the scan to take a measurement.
32
1
2
4
3
Figure 7: Cross-sectional measurement definitions
Figure 8: Data collection tool
33
In the right panel, the blue lines indicate the location of the slices selected for that
particular day (which are not usually chosen consistently from day to day). The green lines
shows the corresponding slice which is seen in the left panel. In order to maintain the
consistency of the data for each patient, the center of each cervical vertebral body was chosen as
a bony landmark. Most of the studies done with H&N patients, especially those evaluating setup
error and uncertainty use the cervical vertebra as landmarks, therefore this was the reasoning
behind using them in this study. Measurements were taken at the center of each vertebral body,
extending from C1-C5. There are 7 cervical vertebrae, however the shoulders appear around C5
or C6 for most patients, and it was found that the information taken at that level is not very
useful for modeling.
Because the images were acquired at a 3-mm slice thickness, one is able to see parts of
each vertebral body throughout the slices. However, to maintain consistency, the center of each
vertebra was located and used as the reference point for taking a measurement. In other words,
measurements were taken where each vertebra appeared intact. Once the point of reference is
established, the tool automatically takes the four lateral measurements and then the user can save
the values to a spreadsheet. The process is repeated for all available vertebra and then for each
daily MVCT scan. As a note, data for some of the vertebra was missing due to some patients not
being scanned with the same consistent set of slices each day. This was corrected for by linearly
interpolating between points.
34
3.1.2 Reduction of Dimensionality
The dimensionality of all data sets is extremely high, so all data was broken up for each
vertebra. For example, patient 1 was treated for 40 treatment fractions and 4 lateral
measurements were taken for all 5 vertebra. If the data is separated by vertebra, then the results
are five 40X4 matrices (4 measurements at each observation). If separated by measurement, then
the result is four 40X5 matrices. The next consideration was the number of models that should be
created. It was assumed that, instead of combining all measurements from all vertebrae together,
a more meaningful approach would be to create 4 models, one for each cross-sectional
measurement. The rationale for this approach will be discussed later in the results section.
Even after partitioning the data to build 4 different models, a large amount of data still
exists, so a trimmed mean was performed for all measurements with respect to each vertebra in
order to reduce the dimensionality of the data. The built-in MATLAB function “trimmean” was
used for this process, where for a matrix input X, the output m is a row vector containing the
trimmed mean of each column of X. The function works by calculating the mean of X, while
excluding the highest and lowest (percent/2) percent, which is a scalar between 0 and 100.
The trimmed mean is a robust estimate of the location of a sample. If there are outliers in
the data, the trimmed mean is a more representative estimate of the center of the body of the data
than just the mean. The highest and lowest 25% of the data was excluded, meaning that only half
of the data was used to calculate a new (condensed) representation of each measurement. This
value was chosen because visual inspection of the data showed that usually there were only two
(at most) outlying measurements whose trends do not follow the other data.
35
To clarify this operation, consider the example presented in Figure 9, which shows an
example of how the trimmed mean was performed to reduce the dimensionality of the data. First,
a 2nd order polynomial line was fitted for each measurement taken (totaling 16 lines per patient)
because most of the data visually followed this type of trend. Next, the regression coefficients for
each line were calculated and then a trimmed mean was performed in order to get a new set of
regression coefficients. Finally, these new coefficients are used to calculate a line which
represents the underlying trend for the previously existing measurements.
Figure 10 illustrates the results from performing a trimmed mean for the measurements
taken at the C1-C4 levels. It can be seen that the dimensionality of the data is reduced while still
preserving a representation of the underlying trend of the rest of the data. The “trimmed mean”
line shown in BLUE STARS in the figure was plotted as a result of the new calculated regression
coefficients. The other lines are simply shown to illustrate how the new line is a valid
representation of the original data.
Visual inspection of the data allowed for the detection of outliers, meaning those lines
whose slopes did not follow the rest of the data. For example, Figure 11 shows one patient's
cross-sectional measurements over a period of 46 treatments. It illustrates how the measurement
taken at C1-C3 followed the same decreasing trend (and whose slope was negative), but the
measurement at C4 showed an increase at the beginning of treatment but then eventually a
decrease.
36
Example Set of Regression Coefficients
Figure 9: Numerical example for calculating a trimmed mean
Remove Highest and Lowest 25% of
(Outlying) Data and Take the Mean to get new Regression Coefficients
0009.00577.09755.4
0008.00526.05238.5
0011.00647.06171.6
0002.00249.02622.7
0009.00551.00705. 6
37
Figure 10: Trimmed Mean Performed
0 5 10 15 20 25 30 35 40 45
6
7
8
9
10
11
Fraction Number
Cro
ss S
ectio
nal M
easu
rmen
t(cm
)
Trimmed Mean
Vert1
Vert2Vert3
Vert4
Figure 11: Illustration of removing outlying data
38
To summarize, a trimmed mean was performed for all measurements, and the
dimensionality of the data was reduced. So now for a patient with R number of treatment
fractions, the dimensionality is reduced from four matrices with a dimension of [RX5] to four
[RX1] vectors. This results in just four measurement lines per patient instead of using the
original 16. It should also be noted that all measurements taken at vertebra 5 were excluded due
to a lack of patient data and non-contributory support for the remainder of the data. Also, from
this point forward the term “measurement (number) x” will refer to the resulting line from which
a trimmed mean was performed, where x is one, two, three, or four.
3.2 DESCRIPTION OF THE DIAGNOSTIC PROCESS
The first part of the IGRT tool is intended to help in identifying patients who would
require/benefit from adaptive therapy, meaning either the construction of a new mask or the
creation of a new treatment plan. After the first few measurements are taken, an estimate needs
to be made as to whether or not (within some amount of confidence) a patient might need ART.
Because the physicians (i.e. oncologists) do not always have the ability or the time to evaluate
each patient’s daily MVCT scan for changes, this part of the tool allows for taking measurements
and then reporting back to the doctor if closer attention is required.
In order to correctly notify the oncologist of a potential problem, there needs to be some
sort of criterion that, when reached, would be considered a means for implementing ART,
whether it be to make a new mask or develop a better radiation treatment plan. According to the
physician who treated all of the H&N patients in this study, a change of 0.5 cm in any of the four
measurements would be considered a big enough change to require extra attention. Typically in a
39
clinical radiation therapy setting, half of a centimeter is considered the threshold for
repositioning patients and thereby reducing setup and treatment error. Therefore, using the same
value was the rationale behind selecting a cross-sectional measurement threshold.
3.2.1 Tools for Diagnosis
The next section will describe the methods used for detecting trends in the cross-sectional
measurement data. The ultimate goal of the diagnostic part of the IGRT tool is to identify those
patients who will meet the physician-specified criteria for re-evaluation of treatment. The answer
to the question for this part of the tool is a simple "yes" or "no."
Trend Detection
Visual inspection of the data shows that most patients’ cross-sectional measurements
either decreased to some extent or had basically no change over the course of treatment. A
decrease is acceptable as along as it does not cause the immobilization device to become
ineffective, but if the decrease is too severe, the patient might need to have a new mask made
and/or a new treatment plan developed.
It was hypothesized that patients who had significant decreasing trends in their cross-
sectional measurements would be candidates for adaptive therapy. The data from each individual
measurement was tested using the Mann-Kendall (MK) test [Dietz & Killeen 1981], which is a
non-parametric multivariate statistical test used for the detection of monotone trends. The test
considers whether the cross-sectional measurement values tend to increase or decrease with time.
40
As opposed to parametric trend detection methods, the MK test makes no assumption with
regards to the functional form of the trend.
For the MK test formalism, let: (X1, X2…Xn) be a sequence of measurements observed
sequentially over time. The hypotheses to be tested are:
H0: the observations are randomly ordered;
H1: the observations have a monotonic trend over time.
The test statistic is given by:
KX =
ji
ij XXsign )( (3.1)
If H0 is the true test statistic, then KX is asymptotically distributed according to the Normal
distribution N (0, σ2), where the variance is given by:
σ2 = 18
)52)(1( nnn (3.2)
A confidence interval of α = 0.95 was used for each test.
The Mann-Kendall test was the first technique used with the data. A few patients’
measurements displayed no change over the course of treatment. However, because most patients
experienced some sort of decline in their measurements, the test basically identified all patients
as those with trends. This information is not very useful in terms of identifying those patients
whose measurements are significantly decreasing, so another method, rates of change, was used
to distinguish between slightly and very “trendy” data.
41
Change in Measurement
Another method for evaluating trends, or behavior of the data, is to simply look at the rate
of change, or the change in each measurement over a given period of time. As mentioned before,
because most of the data follow a decreasing trend, a 2nd order polynomial was fitted, and the
regression coefficients were calculated. Once the regression coefficients are known for the first
few observations, the data can then be extrapolated out to see where (if at all) the threshold of 0.5
cm is crossed for any of the four measurements. If the threshold is predicted to actually be
crossed, then a flag should be put up to say that a patient’s measurements will cross 0.5 cm at
some point in the future.
To illustrate this method we will first examine the true patient data. Figure 13 shows the
four true measurements for Patient 8. Recall that these lines are the result of reducing the
dimensionality of the data, and were calculated by using a trimmed mean technique on the
regression coefficients. The data lines are shown as BLUE stars and the RED dotted lines were
calculated by subtracting 0.5 cm from the patient’s first data point and then plotted straight
across the figure's x-axis. The crossing point, shown as MAGENTA x’s is the point where the
measurement (in BLUE) changes by 0.5 cm (RED).
For example, measurement one in the figure begins with a value of approximately 7.4
cm. Also recall that measurement "one" was taken laterally from the center of the patient’s head
to the right side of the face, which is represented as the solid red line in Figure 12. Therefore, if a
measurement of the cross-section is taken and is less than 6.9 cm, then the physician-specified
criterion has been met, and it is possible that the patient will require adaptive therapy. This, of
42
course, is at the discretion of the physician, but it is necessary to at least communicate that the
patient is undergoing significant anatomical changes so that an informed decision can be made.
One case where the oncologist might not request a new mask is when a measurement
crosses by 0.5 cm, but the patient only has a few treatments remaining. This can be seen in
Figure 14 where Measurement One for Patient 8 appears to cross the threshold at 27/30
treatments, and Measurement Two crosses at 26/30. The feasibility of constructing a new mask
should also be taken into account; however this issue was not addressed and is also at the
discretion of the doctor.
Other patient demographic data, such as weight loss, primary disease site, disease stage,
and chemotherapy were considered useful in making a diagnosis. For example, if a patient lost a
certain percent of their overall body weight after the first few treatments, then this could be used
as an indicator along with cross-sectional measurement changes for making a diagnosis.
Appendix B contains a table which reports the weight loss data that was collected for each of the
30 patients. When possible, weights at the beginning and end of treatment were recorded and the
total percent of body weight lost was calculated.
Figure 12: Depiction of measurement “one”
1
43
Figure 13: All measurements changed by 0.5 cm
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Two
Trimmed Mean Line
Crossover Line
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Trimmed Mean Line
Crossover Point
Patient 8
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
44
Figure 14: No measurements cross the 0.5 cm threshold
5 10 15 20 25 30 35 40 45 50
3.5
4
4.5
5
5.5
6
6.5
7
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35 40 45 50
3.5
4
4.5
5
5.5
6
6.5
7
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Two
Trimmed Mean Line
Crossover Line
5 10 15 20 25 30 35 40 45 50
3.5
4
4.5
5
5.5
6
6.5
7
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Trimmed Mean Line
Crossover Point
Patient 18
5 10 15 20 25 30 35 40 45 50
3.5
4
4.5
5
5.5
6
6.5
7
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
45
3.2.2 Measure of Reliability
In order to provide meaningful results from the diagnostic part of the tool, some sort of
reliability measure is needed to provide confidence with which a decision is to be made. In other
words, if a patient’s initial measurements suggest that the 0.5 cm threshold will eventually be
crossed, then “how sure” are we, that a request for re-evaluation should be made to the
oncologist. This is important from an efficiency standpoint, in that there is no point in wasting
the doctor’s time, essentially, unless the reliability is high that a measurement will cross the
threshold.
Traditional reliability analysis involves the collecting of statistical information which can
reveal the underlying mechanism with which a system will fail. Therefore, a correlation analysis
was carried out for the measurements taken at each vertebra to see if this information could be
used as a measure of reliability. As a note, the built-in MATLAB function corr.m was used to
calculate all correlation coefficients.
All of the collected data was done so retrospectively which allowed for the analysis of
completed data sets. Information was also available concerning which patients had large
reductions in their cross-sectional measurements, as well when their measurements changed by
more than 0.5 cm. This information was used in the modeling process discussed in the next
section, and it was shown that, as more observations (measurements) were made, the correlation
of the data increased, and the percent error with which a prediction was made then decreased.
Therefore, the correlation information proved to be a valid means for reporting preliminary
reliability, and this information will be utilized later for an overall measure of reliability.
46
3.3 DESCRIPTION OF THE PROGNOSTIC PROCESS
As mentioned before, when a patient loses weight or has significant anatomical response,
normal structures may shift into areas of higher dose or the actual tumor volume may have
changed so much that it is no longer receiving the complete radiation prescription. The questions
to be answered in this work can be thought of as part of a prognostic process, where, given a
current state and point in time, predict the future state at the correct time. The area of prognostics
is not the main focus of this dissertation. However, the prognostic framework used to solve
common problems in industry can be applied to the data at hand in order to make a prediction.
For example, a classic prognostic system in industry involves predicting when all or part
of a machine will fail. In this process, statistical data that are collected for the object of interest
are used to evaluate some degradation parameter, which leads to an understanding of the
underlying failure mechanism. Then, the best-suited prognostic model is chosen so that a reliable
prediction for failure can be made. Common terms used in prognostic systems are “remaining
useful life,” “degradation process,” and “time to failure,” which have similar meaning to the
terms used when describing a patient’s anatomical response to radiation. This type of system is
very similar to the one presented in this dissertation. Up to this point, statistical correlation data
has been acquired and the underlying trend of the data is understood. The next and final step in
the process is to make a reliable prediction about when a patient would require adaptive radiation
therapy.
This section will describe how a time prediction can be made, which is the basis for the
second part of the IGRT tool. Once an answer to the first question is found, then the data and its
underlying relationships can then be used to make a prediction about at what point in time the
47
patient's cross-sectional measurements will (if ever) cross the threshold of 0.5 cm. This is of
particular importance because it allows for pre-allocation of time and resources of the clinical
staff to prepare for an adaptive course of treatment. And as a note, the “point in time” is assumed
in terms of the treatment number where the threshold is crossed. In reality, patients are only
treated during a typical work week (Monday – Friday), but the predicted “point in time” is based
on the assumption that a patient is treated everyday of the week. This is a minor detail, however
it is important to clarify that time is really treatment number instead of elapsed number of days.
3.3.1 Prediction Model Selection
The ultimate deciding factor in model selection should be how well a given model can
characterize the data. Two types of modeling techniques, non-parametric and parametric, are
commonly used to make future predictions when given a set of data. A non-parametric model is
defined by historical data and a process for applying the historical data to estimate new query
observations. An example is weighted regression, where the historical data are memory vectors
selected from normal, fault-free system behavior. This non-parametric technique involves
estimating the similarity of a query observation to memory vectors in order to determine weights
and perform a weighted average of the memory vectors. A parametric model, in contrast, is
defined by a set of parameters and a mathematical framework for applying the parameters. An
example is linear regression, where the parameters are the regression coefficients and the
mathematical framework is a known function which uses the parameters to define the behavior
of the model.
48
Kernel Classification
Initially, the task of identifying patients who required adaptive therapy was considered
in terms of a classification problem, where data from "old" patients were used to predict the class
of "new" patients. For example, if one H&N patient exhibited significant change in the first few
treatments, then that patient would be classified differently than one whose cross-sectional
measurements showed little change. Then once a database of pre-classified patients was formed,
new patient data could be compared to old data in order classify that new patient. For this
problem, the classes were "abnormal" or "normal", where abnormal patients' measurements
significantly decreased over the course of treatment, where normal patients' measurements did
not. So initially a non-parametric kernel regression technique was used to predict which patients
would require adaptive radiation therapy.
The basis of kernel regression is to estimate a response using a weighted average of point
in a training set, which are local to the query point. For this study, the training set contained all
of the historical cross-sectional measurement data. This data had been previously evaluated so
that each patient was already classified as abnormal or normal. Since the classes are already
known, the kernel classification model can learn the relationships present in the historical data.
This type of model is slightly different than typical types of kernel regression models because the
output is not a prediction of cross-sectional measurements, but the class to which a certain
patient belongs (abnormal or normal). So, a patient whose measurements cross the 0.5 cm
threshold would be classified as abnormal, whereas those who do not would be considered
normal.
The kernel classification framework is comprised of three basic steps:
49
1. The distance of the query input (new data) to each of the vectors in the memory
matrix (historical data) is calculated. There are many distance functions available,
but the most common is the Euclidean distance, also known as the L2-norm. For a
single input, the Euclidean distance for the ith input and a query is given by:
i iqiXqXd
2)(),( (3.3)
where: X is the memory vector of historical data,
q is the query observation
For a single query vector, this calculation is repeated for each of the memory
vectors which then results in a matrix of distances.
2. The distances are then supplied as inputs into the kernel function, which converts
the distances into weights. A kernel function is used to transform the distance
between the query point and observation into a similarity, or normalized weight.
In general, query data points which are “close” to the memory matrix data points
are weighted more heavily, so large distances should have small weights and
small distances will have large weights. One commonly used function is the
Gaussian kernel, known as:
22
2
2 2
1h
d
eh
dK
(3.4)
50
where: d is the distance calculated in Equation 3.5,
h is the kernel's bandwidth
The kernel's bandwidth, h, is used to control which distances are considered "local." If a
bandwidth is too large, the predictions are over-smoothed and all classifications will be the same,
and a small bandwidth will restrict the number of available training points in the vicinity of the
test points.
3. The last step is to use the weights calculated in Equation 3.6 to make a prediction. For
classification problems, the standard kernel estimation technique is modified to
estimate the probability of the new dataset, belonging to one of two classes. This
modified type of kernel classification comes from an application for classification
using kernel product estimators and a decision rule [Cooley & MacEachern 1998].
The final class estimate is made with the following equation:
PRk(x) =
n
ii
n
ii
ki
hxxK
hxxKT
1
1
)(
)/)((
)/)(( (3.5)
where: xi is the ith training observation,
Ti is 1 if the response is a member of set k and 0 otherwise,
51
K is the Gaussian kernel function (Equation 3.6)
n is the total number of training observations,
Extrapolation-Based Prediction
In contrast to the kernel regression method, a parametric extrapolation-based model was
constructed, where a polynomial function and its regression coefficients are used for model
development. The equation for a second-order polynomial, or quadratic, is commonly known as:
y(t) =b0 + b1t + b2 t2 (3.6)
where b0, b1, and b2 are the linear regression coefficients. The function y(t) is defined to be an
approximating curve that provides the best fit of the collected measurements. Once the
regression coefficients are known for certain measurements, a prediction is made through
extrapolation of y(t) in a moment of time (t) in the future. The rationale for using this type of
model is that the underlying characteristic of the data appears to decrease gradually over time;
therefore complex modeling is not necessary and a generic parametric model is well-suited for
the data.
3.3.2 Reliability Metric
The term "reliability" in this dissertation is similar to the reliability of a machine (and its
parts) whose performance is monitored for signs of failure. The concepts of “remaining useful
life” and “time to failure” can be thought of as “effectiveness of the immobilization device” and
52
“closeness to crossover.” In the diagnostic part of the IGRT tool, correlation of the data was
discussed as a reliability measure after identifying patients who would need a new mask.
Because the prognostic part of the tool involves making a prediction as to when a new mask or
treatment plan is required, another measure, the "closeness to crossover" was combined with
correlation to provide confidence or some reliability for the time predictions. An example of how
this extra measure was calculated will be discussed later in this section.
The effectiveness of the immobilization device is affected by great reductions in cross-
sectional measurements. For example, at the beginning of a patient's treatment a new mask is
made, and it is molded specifically for that patient. It is assumed that this mask will remain tight
and fitted on the patient's head each day, however if anatomical changes or weight loss occur, the
mask could become loose enough that the patient is able to move their chin and/or head around
inside the mask. This is undesirable for two reasons:
1. All patients have marks put on their skin so that an alignment can be made using the
stationary in-room lasers. For head and neck patients, these marks are placed on the
outside of the mask, which also are used for patient setup.
2. Radiation is being delivered at the head, near critical structures such as the brain,
eyes, nerves, and the spinal cord. Once the mask is no longer immobilizing the
patient, a new one should be made in order to avoid irradiating these structures.
The remaining useful life or the effectiveness of the mask is assumed to be 100% at the
beginning of treatment. This means that the mask is correctly made and is tightly fitted on the
53
patients head to prevent movement. However, if significant changes in cross-sectional
measurements occur, then the effectiveness, and therefore the “remaining life” of the mask
decrease as well. An effectiveness value is not used as a reliability measure of this system,
although it is directly related to the closeness to crossover value which is used to determine
reliability of a prediction.
As discussed previously, the regression coefficients for the trimmed lines were calculated
and then these were the basis for a prediction. Also, the correlation coefficients of the
measurements proved to be an effective measure of reliability. However, because true error can
not be assessed during the course of a patient's treatment, the "closeness to crossover" value can
be used as part of the reliability metric.
The “closeness to crossover” value is actually just the answer the following question:
How close in proximity are the cross-sectional measurements to changing by more than 0.5 cm?
It can be thought of as the used “life” of the facemask, where the values range from 0 to 1, with 0
signifying that the mask has 100% of its “life” left and 1 meaning no “life” left. It is calculated
according to Equation 3.7:
Closeness to Crossover =
C
C
t
tt1 (3.7)
where Ct
is the predicted time of crossover and t is the time at which a prediction is being made
(the current time). To clarify, the extrapolation model is given 15 observations (starting at
observation 1) for each of the four measurements. After the 15th observation, the model predicts
a crossover time (for each measurement) based on the change in measurement for the first 15
54
observations, and recall that the prediction is made by extrapolating out the regression
coefficients. When the change in measurement is greater than 0.5 cm, then the model finds the
point in time where this happens, or Ct
. The predicted “closeness to crossover” value is then
calculated by plugging in Ct
and the current time point (t) into Equation 3.6. Consider Figure 15,
an example for just one of the four measurements for Patient 8.
The model has predicted that the measurement will have changed by 0.5 cm at Ct
= 15.
This can be seen because the measurement taken at the first time step is 7.265, so (7.265 – 0.5)
cm = 6.765. The first value to be equal to or less than this value is 6.742 cm, located at time 15.
The “closeness to crossover” is calculated for each real time step t, where a value of 1 indicates a
predicted threshold crossover. A new crossover value is calculated every time an observation is
added, so a “closeness to crossover” value can be found for any time step through the course of
treatment.
Because the correlation coefficients were already proved as a valid measure of reliability,
closeness to crossover values were averaged with the correlation values, which resulted in a
value for overall reliability. Figure 16 illustrates how the correlations of the data as well as the
closeness to crossover values are quite similar and follow the same trend. The x-axis is the
treatment number and the y-axis is a scale from -1 to 1, where at zero, neither the data is very
correlated nor are the patient’s measurements close to crossing the 0.5 cm threshold. But, one
can see that both increase together. The closeness to crossover is a useful addition to reporting
reliability because it is possible for the data to be highly correlated without being close to
55
crossing the 0.5 cm threshold. The next section will discuss the results from the diagnostic and
prognostic parts of the IGRT tool.
56
1st 10 Observations Change in Measurement
10
9
8
7
6
5
4
3
2
1
156.7
208.7
251.7
283.7
305.7
317.7
319.7
311.7
293.7
265.7
110.0
057.0
015.0
018.0
040.0
052.0
054.0
046.0
028.0
00.0
Next 10 Predicted Values “Closeness to Crossover”
20
19
18
17
16
15
14
13
12
11
076.6
229.6
373.6
506.6
629.6
742.6
845.6
938.6
020.7
093.7
1 -
15/)1515(
.
.15/)1215(
15/)1115(
=
0.1
.
.
80.0
733.0
Figure 15: Illustration of calculating the Closeness to Crossover value
57
Figure 16: Correlation vs. Closeness to Crossover
5 10 15 20 25 30-1
-0.5
0
0.5
1Measurement 1
Closeness to Crossover
Correlation
5 10 15 20 25 30-1
-0.5
0
0.5
1Measurement 2
5 10 15 20 25 30-1
-0.5
0
0.5
1Measurement 3
5 10 15 20 25 30-1
-0.5
0
0.5
1Measurement 4
Treatment Number
58
4 RESULTS
The next section will present the results from the two components of the IGRT tool. The
diagnostic results identify those patients who will require adaptive therapy (i.e. a new facemask
or new treatment plan), and the prediction results relay the point in time when the cross-sectional
measurement data crosses the threshold of the physician-specified criteria. Because the goal of
this tool is to evaluate new patients for whom no previous data exists, these predictions start after
the patient has received 15 treatments and then continual predictions after each proceeding
treatment. This process ends after a reliable time-crossing prediction is made.
4.1 DIAGNOSIS The first set of results identifies (with some reliability) which patients’ measurements
will cross the 0.5 cm threshold at some point in the future. As discussed previously, it is
necessary to evaluate each measurement individually because the physician is interested in
knowing if any of the four measurements change by more than 0.5 cm. It would be helpful to
diagnose patients as early as possible while still being somewhat sure, therefore a reliability
value will be given after each of the diagnoses.
4.1.1 Data Set Selection
Before displaying the results from the diagnostic process, it is first necessary to address
the isssue of data selection for this part of the tool. As mentioned previously, demographic
information was collected and evaluated. However, the data were very random and not
59
standardized. To clarify, the data were collected by reading through each patient's chart in an
attempt to find out as much demographic information as possible. However, it was found that
patient charts are often unorganized and it impossible to ensure that all demographic information
was completely documented.
One example of this was that some patients’ weights were not recorded consistently and
were also only taken once a week at most. Often, there would be no record of beginning weight
or the first record would occur after the first 10 treatments. Therefore, weight loss could possibly
be an indicator that a patient will require adaptive therapy, but having inconsistent and missing
data for this work proved the opposite. The rest of the demographic information proved to be
unhelpful as well. It is believed that relying on this type of data would only be possible for
patients who were under a clinical trial where all variables are under strict control, whereas the
patients in this study were just chosen at random for evaluation. Therefore, only the cross-
sectional measurement data was used for training and testing.
Another data set selection issue was also mentioned previously, and that is the one of
how many models should there exist for each patient. All four lateral measurements were plotted
for C1-C4. Figure 16 shows the four measurements taken at the C3 level for one of the patients.
It is apparent that there is a wide range in physical measurement, from almost 8 cm to 6.5 cm.
Also, after visual inspection of the data, each vertebra's measurements did not seem to follow the
same trend always. Therefore, instead of using the 4 lateral measurements for each vertebra as
data for the model, all of the "ones", "twos", "threes", and "fours" were pulled out for each
vertebra, and these were the data that were used for modeling.
60
Another advantage to taking this approach is that the model is given 4 measurements that
should all have about the same trend, therefore having some redundant information provides for
robust modeling and a better chance for making an accurate diagnosis/prediction. Also,
symmetry of measurement change can be visually evaluated. For example, if meaurements 1 and
2 (which are on the right side of the face) are changing rapidly and measurements 3 and 4 are
changing slowly, one could assume that this was the result of asymmetric anatomical response,
whereas if all 4 measurements are changing rapidly, one could identify that patient as having a
symmetric anatomical response, which would most likely be attributed to overall weight loss.
Distinguishing between tumor response and overall weight loss was not one of the major
contributions in this work, however the tool does allow for plotting any of the data for any of the
patients’ measurements. Therefore, the physician could visually inspect the plots if he/she were
interested in knowing what type of anatomical response is occurring.
4.1.2 Diagnosing ART Candidates
Two methods, trend detection and change in measurement, were previously discussed as
possible ways to identify patients who will require adaptive therapy in the future. The results
from these two methods will now be discussed.
Trend Detection
The Mann-Kendall test for trend detection worked. However, sometimes when a trend
was detected, it was not very significant in terms of a patient requiring ART. As stated before,
most patients will undergo some kind of anatomical change over the course of therapy, and so
61
the MK test did return accurate results because it simply tests for a decreasing trend. Therefore,
the results were visually inspected and a “professional decision” was made as to which patients
were considered abnormal or normal, using a threshold criteria of a 0.5 cm change over the entire
course of treatment. As a note, each of the four measurements was kept with their respective
vertebra when evaluating trends.
Patients were classified as either normal or abnormal; where abnormal were those whose
measurements crossed the 0.5 cm threshold and the normal did not. Figure 17 shows an example
of one patient’s cross-sectional measurements over the course of 32 treatments, and Figure 18
shows a different patient’s measurements over the course of 45 treatments. The first figure
visibly shows a significant decreasing trend over the course of treatment and would be
considered as abnormal. However the second figure only shows a slight decrease over time, so
this would be normal change in measurement, meaning not enough change for a patient to
require adaptive therapy. As mentioned before, the MK test can not provide qualitative results
about the magnitude with which a trend is occurring, only if the data is “trendy” or not.
Therefore using calculated changes in measurement was the method used for diagnosis.
62
0 5 10 15 20 25 30 354.5
5
5.5
6
6.5
7
7.5
8
8.5
Fraction Number
Cro
ss-S
ectio
nal M
easu
rem
ent(
cm)
Measurement 1
Measurement 2
Measurement 3
Measurement 4
Figure 17: Significant decreasing trend for Patient 14
0 5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
Fraction Number
Cro
ss S
ectio
nal M
easu
rem
ent(
cm)
Measurement 1
Measurement 2
Measurement 3
Measurement 4
Figure 18: Slightly decreasing trend for Patient 24
63
To reiterate the method, 4 different lateral measurements were taken at the C1-C4
vertebral levels and then each measurement was taken out from its respective vertebra level. The
result is having all of the "ones", "twos","threes", and "fours" put together to create 4 different
models for each patient.
Change in Measurement
The changes in cross-sectional measurement were first calculated by starting with the
first 15 observations and extrapolating out the trimmed mean lines (i.e. each of the four
measurements). A prediction is made about whether or not we think any of the measurements
will cross the 0.5 cm threshold. Also, the average correlation coefficients (which are squared and
do not include any negatively correlated values) are reported, giving a measure of the prediction
reliability.
All true patient data was tested to see which patient’s (and each one specifically)
measurements crossed the 0.5 cm threshold. Tables 2-4 show the results from that testing,
including the correlation values for the data, and all of the data from these tables (excluding
correlation values) were plotted for each patient and can be found in Appendix B. For each
measurement (1-4) the true crossing point is listed along with how well the data was correlated.
Another important value to note is the total number of treatments. For example, Measurement 1
for Patient 12 shows a true crossing time at 46/46 treatments, and the correlation of the data has a
value of 0.955. In the prognostic part of the tool, if the model predicts that the measurement
never crosses the threshold, there would be some error associated with that prediction; however
the true point of crossing was at the end of treatment. Therefore, the total number of treatments
64
should be noted when evaluating the performance of the prediction model, which will be
presented in the next part of this section.
65
Table 2: Crossover data for Patients 1-10
Patient Number
Measurement Number
Crossover Point (Treatment Number)
Total Number of Treatments
Reliability Value
Reliable (Y/M/N)
1
1 13
40
0.885 Y 2 15 0.990 Y 3 26 0.948 Y 4 11 0.962 Y
2
1 13
44
0.922 Y 2 22 0.995 Y 3 39 0.985 Y 4 25 0.851 Y
3
1 -
38
0.838 Y 2 - 0.910 Y 3 - 0.919 Y 4 33 0.971 Y
4
1 -
33
0.883 Y 2 32 0.441 M 3 - 0.891 Y 4 - 0.959 Y
5
1 -
39
0.279 N 2 - 0.234 N 3 - 0.858 Y 4 - 0.760 Y
6
1 -
36
0.920 Y 2 - 0.720 Y 3 - 0.950 Y 4 - 0.556 M
7
1 7
34
0.986 Y 2 11 0.963 Y 3 28 0.806 Y 4 9 0.924 Y
8
1 27
32
0.955 Y 2 26 0.988 Y 3 17 0.981 Y 4 20 0.984 Y
9
1 -
42
0.568 M 2 36 0.991 Y 3 32 0.914 Y 4 - 0.502 M
10
1 8
30
0.848 Y 2 - 0.859 Y 3 - 0.893 Y 4 8 0.570 M
66
Table 3: Crossover data for Patients 11-20
Patient Number
Measurement Number
Crossover Point (Treatment Number)
Total Number of Treatments
Reliability Value
Reliable (Y/M/N)
11
1 21
39
0.981 Y 2 - 0.652 Y 3 30 0.985 Y 4 22 0.960 Y
12
1 46
46
0.955 Y 2 42 0.997 Y 3 - 0.821 Y 4 - 0.746 Y
13
1 -
42
0.838 Y 2 22 0.974 Y 3 16 0.856 Y 4 15 0.711 Y
14
1 7
38
0.998 Y 2 12 0.995 Y 3 14 0.971 Y 4 8 0.958 Y
15
1 14
37
0.995 Y 2 20 0.994 Y 3 13 0.992 Y 4 11 0.990 Y
16
1 35
35
0.792 Y 2 - 0.560 M 3 21 0.720 Y 4 10 0.978 Y
17
1 43
46
0.729 Y 2 16 0.611 Y 3 20 0.503 M 4 32 0.941 Y
18
1 -
51
0.833 Y 2 - 0.402 M 3 - 0.501 M 4 - 0.559 M
19
1 20
40
0.950 Y 2 - 0.465 M 3 - 0.892 Y 4 - 0.414 M
20
1 13
33
0.989 Y 2 21 0.986 Y 3 23 0.930 Y 4 31 0.942 Y
67
Table 4: Crossover data for Patients 21-30
Patient Number
Measurement Number
Crossover Point (Treatment Number)
Total Number of Treatments
Reliability Value
Reliable (Y/M/N)
21
1 31
40
0.939 Y 2 38 0.937 Y 3 36 0.915 Y 4 24 0.970 Y
22
1 28
38
0.953 Y 2 14 0.995 Y 3 20 0.981 Y 4 25 0.849 Y
23
1 10
29
0.698 Y 2 13 0.575 M 3 - 0.939 Y 4 - 0.845 Y
24
1 34
41
0.967 Y 2 - 0.938 Y 3 38 0.545 M 4 - 0.942 Y
25
1 -
38
0.881 Y 2 - 0.356 M 3 - 0.555 M 4 - 0.450 M
26
1 -
39
0.328 N 2 - 0.903 Y 3 - 0.427 M 4 - 0.349 M
27
1 -
47
0.704 Y 2 - 0.920 Y 3 - 0.778 Y 4 - 0.994 Y
28
1 -
45
0.902 Y 2 37 0.978 Y 3 - 0.593 M 4 17 0.910 Y
29
1 -
41
0.473 M 2 - 0.900 Y 3 - 0.367 M 4 16 0.986 Y
30
1 -
46
0.602 Y 2 - 0.977 Y 3 - 0.448 M 4 - 0.653 Y
68
Table 5 shows the results for diagnosing those patients who would have benefited from
adaptive therapy, whether it was receiving a new facemask or a new treatment plan. In order to
make a diagnosis, the first 15 observations were given. This is an assumption that was made due
to the fact that all 30 patients underwent an average of 39 treatments (a total of 1180). It would
be desirable to make a diagnosis early in treatment, so beginning with 15 observations seemed
feasible since this is just below half of the average number of treatments. As a note, because
there are 4 measurements per patient, and each measurement was modeled individually, there are
actually 120 total diagnoses being made.
There are two different results for the diagnostic process. The first is those patients who
will require a new mask at some point during their treatment. The table below shows which
patients were believed to need adaptive therapy at some point in their treatment given the first 15
observations, indicated by Y/N. Secondly, if a decision could not be made in the first 15
observations (indicated by a "?"), then the point at which a reliable diagnosis could be made is
shown as the treatment number/total number of treatments. There were only 3 measurements,
shown as an X in a RED box, which never could produce a reliable diagnosis.
69
Table 5: Diagnosis of ART candidates
Patient Number
Meas. Number
Cross 0.5 cm after
15 tx (Y/N)
Tx when
reliable
Patient Number
Meas. Number
Cross 0.5 cm after 15 tx (Y/N)
Tx when
reliable
Patient Number
Meas. Number
Cross 0.5 cm after
15 tx (Y/N)
Tx when
reliable
1
1 Y
11
1 Y
21
1 ? 29/40 2 Y 2 ? 32/39 2 N 3 Y 3 ? 24/39 3 N 4 ? 20/40 4 ? 17/39 4 ? 19/40
2
1 Y
12
1 ? 16/46
22
1 ? 25/38 2 Y 2 ? 18/46 2 Y 3 Y 3 ? 19/46 3 Y 4 ? 33/44 4 ? 30/46 4 ? 25/38
3
1 N
13
1 N
23
1 Y 2 N 2 ? 16/42 2 Y 3 N 3 ? 38/42 3 N 4 N 4 ? 23/42 4 N
4
1 ? 23/38
14
1 Y
24
1 ? X
2 ? 32/38 2 Y 2 N 3 N 3 ? X 3 N 4 N 4 ? 20/38 4 N
5
1 N
15
1 Y
25
1 ? 30/38 2 N 2 Y 2 ? 25/38 3 N 3 Y 3 N 4 N 4 Y 4 ? 25/38
6
1 N
16
1 N
26
1 ? 20/39 2 N 2 N 2 ? 20/39 3 ? 17/36 3 ? 17/35 3 ? 20/39 4 ? 20/36 4 Y 4 N
70
7
1 Y
17
1 ? 25/46
27
1 ? 16/47 2 Y 2 Y 2 ? 18/47 3 ? 25/34 3 ? 23/46 3 ? 16/47 4 Y 4 Y 4 ? 16/47
8
1 Y
18
1 ? 26/51
28
1 N 2 ? 26/32 2 N 2 ? 21/45 3 Y 3 N 3 ? 28/45 4 ? 20/32 4 N 4 Y
9
1 ? 25/42
19
1 ? 27/40
29
1 N 2 ? 20/42 2 ? 20/40 2 ? X 3 ? 37/42 3 N 3 ? 26/41 4 N 4 N 4 Y
10
1 Y
20
1 Y
30
1 ? 27/46 2 ? 19/30 2 Y 2 ? 21/46 3 ? 17/30 3 ? 30/33 3 ? 18/46 4 Y 4 ? 20/33 4 ? 19/46
71
The diagnosis results show that approximately half (61/120) of the measurements
predicted that patients would need ART within the first 15 observations and 28/120 required 20
observations to make a reliable diagnosis. Therefore, 74% of patients' measurements accurately
diagnosed patients would need adaptive therapy within the first 20 observations. While it does
take 20 observations to have fairly accurate results, it is important to remember that the average
number of treatments that all 30 head and neck patients underwent was 39 treatments.
As mentioned previously, 11 of the 30 patients clearly did not require adaptive therapy
(indicated by dashed lines in Tables 2-4), while 4 were questionable, meaning the crossover
points were very close to the end of their treatment. Therefore, half of the patients more than
likely needed to have a new facemask constructed and/or the development of a new treatment
plan based on the change in any of their four cross-sectional measurements.
4.2 PREDICTION
Obviously early on in treatment, a prediction could be unstable or inaccurate, but as more
observations are added there is more information used to make a prediction. There were a few
questions about the reliability of the data, because as mentioned before, some patients had a
significant amount of data missing from their data sets. Appendix B contains a table which
shows the percentages of data that were missing and then corrected for by linear interpolation.
From the table it can be seen that 9 patients' data consisted of more than 25% interpolated data,
and the reliability in these patient's measurements could potentially reduce the reliability of a
prediction.
72
As discussed before, correlation information was used as part of the reliability measure
for diagnosis, because it was found that percent error decreased as the correlation of the data
increased, and the next figures just illustrate this result. Average percent error and the correlation
of the data were tested for five of the thirty data sets. Figures 19 and 20 show the results from
two of the cases. It can be seen that, in general, as the correlation of the measurements goes up,
the percent error decreases.
73
Figure 19: Correlation vs. Percent Error for Case #1
5 10 15 20 25 30-50
0
50
100
150Measurement 1
Percent Error
Correlation
5 10 15 20 25 30-50
0
50
100
150Measurement 2
5 10 15 20 25 30-50
0
50
100
150Measurement 3
5 10 15 20 25 30-50
0
50
100
150Measurement 4
Treatment Number
74
Figure 20: Correlation vs. Percent Error for Case #2
5 10 15 20 25 30 35 40-50
0
50
100
150Measurement 1
Percent Error
Correlation
5 10 15 20 25 30 35 40-50
0
50
100
150Measurement 2
5 10 15 20 25 30 35 40-50
0
50
100
150Measurement 3
5 10 15 20 25 30 35 40-50
0
50
100
150Measurement 4
Treatment Number
75
4.2.1 Kernel Classification
Once a database of pre-classified patients is formed, measurements for any new patient
can be taken and used to determine if they would be a candidate for ART. For the predictions,
data from 30 patients were included and a leave-one-out cross validation method was used. The
kernel classification model was able to accurately predict those patients whose assumed true
category (class) was abnormal. For the assumed normal cases, 13 were identified as normal and
2 were identified as abnormal. Table 6 illustrates the results in a confusion matrix.
Table 6: Kernel Classification prediction results
15 15 Total
15 2 Abnormal
0 13 Normal
Abnormal Normal
True Category
76
The problems with the kernel classification model are twofold:
1. The database of pre-classified patients was formed by visually inspecting the trend of
patients’ measurements (at each vertebral level) and attempting to classify them by a
“professional opinion.” This was, as mentioned before, because the results from the
Mann-Kendall test reported that all of the data was “trendy” and therefore that all
patients should be classified as abnormal. Because this was known to be false, a
professional decision was made as to which patients were classified as abnormal or
normal.
2. The data used for the memory matrix should have been separated out by measurement
rather than all for each vertebra. Also, the rate of change would have been more
helpful for making a prediction than just using the measurements themselves.
This non-parametric classification technique is useful in industry problems because
relationships of data can be learned and predicted in the future. However, the problem presented
in this dissertation involves human data, which often exhibit unexplainable trends and
unpredictable results. Because the behavior of the data is very particular for a given patient, it
was decided that the employed prediction method should be specific for each new patient rather
than try to make a prediction based on historical patient data.
77
4.2.2 Extrapolation Model
As explained in the methodology section, the basis for the extrapolation model is to take
the first few observations of a patient’s measurements and extrapolate out (using regression
coefficients from the prediction line thus far) to make a prediction about what point in time the
0.5 cm threshold will be crossed. So in the beginning, if the model is given 15 observations, a
time prediction is made, and a new time prediction and reliability value are then given each time
an observation is added.
For this portion of the tool, an "accurate" prediction was determined by evaluating the
variation after a series of time predictions. For example, assume for times 10-15 that the
predictions for crossing were at treatments 12, 13, 14, 13, and 14, respectively. The variation in
these numbers is very low, so the standard deviation for every "last 3 predictions" was used as an
indicator as to how "sure" one is that a crossover (at the 0.5 cm threshold) is about to occur. If
the variation (i.e. standard deviation) in the "last 3 predictions" is less than 3, then the correlation
value and the closeness to crossover value are averaged at that point and reported as the measure
of reliability. If the reliability at that point is high, meaning greater than 0.5, then the predicted
crossover time at that point is assumed to be the correct crossover time. However, if the
reliability value is less than 0.5, the model continues on with predictions until a reliable
crossover time is found.
78
Table 7 only displays results for the 15 patients who should have received adaptive
therapy and how accurately a predicted crossover time was reported after being given the first 15
observations. As mentioned before, reliability measures greater than 0.5 are considered as being
reliable.
Table 7: Reliability for patients requiring ART
Patient Number
Measurement Number
Timely Prediction
Reliability Value
Reliable (Y/N)
1
1 * 0.66 Y
2 * 0.52 Y
3 * 0.37 N
4
2
1 * 0.61 Y
2 * 0.54 Y
3 * 0.34 N
4
7
1 * 0.82 Y
2 * 0.76 Y
3
4 * 0.88 Y
8
1 * 0.76 Y
2
3 * 0.44 N
4
10
1 * 0.57 Y
2
3
4 * 0.99 Y
11
1 * 0.54 Y
2
3
4
13
1 * 0.93 Y
2
3
4
79
14
1 * 0.80 Y
2 * 0.75 Y
3
4
15
1 * 0.89 Y
2 * 0.76 Y
3 * 0.95 Y
4 * 0.88 Y
16
1 * 0.27 N
2 * 0.29 N
3
4
17
1
2 * 0.55 Y
3
4 * 0.62 Y
20
1 * 1.00 Y
2 * 0.61 Y
3
4
22
1
2 * 0.46 N
3 * 0.62 Y
4
23
1 * 0.83 Y
2 * 0.53 Y
3 * 0.36 N
4 * 0.29 N
28
1 * 0.55 Y
2
3
4 * 0.92 Y
80
Because the most important result from the IGRT tool is the time prediction, it is
necessary to evaluate the accuracy, and more specifically in terms of the amount of “lead time”
after making that prediction. For example, suppose at treatment number 10, measurement “one”
(for patient “x”) predicts that the 0.5 cm threshold will be crossed at treatment number 15, given
a reliability value of greater than 0.5. If the measurement however, does not cross until treatment
20, then the staff will essentially have 5 days of “lead time” for preparation. Therefore, it is
desirable to give as much lead time as possible.
The convenient aspect of the tool is that predictions (and reliability values) are
continuously generated for each measurement throughout the course of a patient’s treatment. In
other words, if the model reliably predicts early on (say at treatment 8) that a given measurement
will cross at treatment 15 and then at treatment 15 the measurement has not quite crossed, new
predictions can be made until the variation in time predictions slows. As the variation in time
predictions decreases, the predicted closeness to crossover should increase, thus increasing the
reliability in each prediction.
Tables 8-10 show the prediction results with reliabilities of 0.5, 0.6, and 0.7, respectively.
The results are given in (a) confusion matrices, where the number of predicted and true
crossovers can be seen. Also, the accuracy with which a prediction is made is given for each
class, crossover or no crossover, as well as an overall measure of accuracy. The second part of
the table (b) illustrates the prediction windows, where the average number of days to make a
prediction is given. Another important piece of information is how many days in advance one is
notified that a patient will require adaptive therapy. As mentioned before, this is the key result
for being able to know how much time will be available to pre-allocate clinical time and
81
resources. Therefore, the time was split into: more than 5 days, 1-4 days, and no lead time (0
days).
For example, Table 8 (b) shows that it takes an average of 11 days to make predictions
with a reliability value of at least 0.5. Also, 57% of predictions were given with at least 5 days to
make preparations, 14% with 1-4 days, and 29% with no lead time. Obviously, it is desirable to
know more than 0 days before a patient will require ART. So, Tables 9 and 10 show the results
for making predictions with higher reliability values (0.6 and 0.7). It can be seen that both have
similar results, where a prediction is made (more than 80% of the time) knowing at least 5 days
ahead of time. This means that, perhaps, a reliability of 0.65 should be used when reporting
"highly reliable" predictions. The tables show that, while it does require more days to make a
higher prediction, the amount of significant “lead time” (i.e. more than 5 days ahead) increases
from 57% to 86% with 7 extra observations. This result suggests that predictions should continue
to be made (even after a reliability value of 0.5 is found) until a reliability of close to 0.7 is
given.
82
Table 8: Prediction results for a reliability > 0.5
Reliability > 0.5
Prediction Window
PREDICTED
Class Accuracy (%)
Overall Accuracy (%)
11 obs.
Average number of observations to make a prediction
Crossover No Crossover
57.0% 5 treatments ahead of actual
crossover
TR
UE
Crossover 30 18 62.5%
63.0%
14.0%
1-4 treatments ahead of actual crossover
No Crossover 6 6 50.0%
29.0% No Lead Time (0 treatments ahead)
a) Confusion Matrix b) Prediction windows
83
Table 9: Prediction results for a reliability > 0.6
Reliability > 0.6
Prediction Window
PREDICTED
Class Accuracy (%)
Overall Accuracy (%)
16 obs.
Average number of observations to make a prediction
Crossover No Crossover
90.0% 5 treatments ahead of actual
crossover
TR
UE
Crossover 31 17 64.6%
65.1%
0.0%
1-4 treatments ahead of actual crossover
No Crossover 6 6 50.0%
10.0% No Lead Time (0 treatments ahead)
a) Confusion Matrix b) Prediction windows
84
Table 10: Prediction results for a reliability > 0.7
Reliability > 0.7
Prediction Window
PREDICTED
Class Accuracy (%)
Overall Accuracy (%)
18 obs.
Average number of observations to make a prediction
Crossover No Crossover
82.6% 5 treatments ahead of actual
crossover
TR
UE
Crossover 41 7 85.4%
86.0%
8.7%
2-4 treatments ahead of actual crossover
No Crossover 9 3 60.0%
8.7% No Lead Time (0 treatments ahead)
a) Confusion Matrix b) Prediction windows
85
The next series of figures (Figures 21-28) shows the results for Patient 11. They illustrate
measurements 1-4 being predicted after 10 and then 15 observations. Each figure contains 3
subplots, where the first illustrates the prediction line, predicted crossover point, and true
crossover point. The x-axis for all subplots is the number of treatments and the y-axis for the first
is the cross-sectional measurement value in centimeters.
The second subplot shows the error with which the prediction is made along with the true
closeness to crossover, which was calculated knowing the true point of crossover. For this
subplot, the y-axis ranges from 0 to 1. As discussed previously, the closeness to crossover values
range from 0 to 1, where values approaching 1 indicate that a crossover is about to occur. The
error was calculated as a percent; however it was normalized on a scale of 0 to 1 in order to
compare it to the crossover values. Therefore an error value of 1 really indicates 100% error.
The last subplot shows the predicted value versus the true value of closeness to crossover.
Once again, the y-axis is a scale from 0 to 1, where a crossover, be it predicted or true, equals a
value of 1. The second and third subplots can be interpreted together, where the predicted
closeness to crossover should be indirectly proportional to the error associated with that
prediction. The true closeness to crossover values are just plotted to illustrate how accurate the
model is making a prediction over time, because obviously when a new set of data are presented,
the true crossover point is unknown.
Figures 20 and 21 shows the results from a prediction made after 10 and then 15
observations, respectively. In Figure 20, subplot 1, it can be seen that the predicted line crosses
the 0.5 cm threshold at treatment number 20, indicated as RED diamonds, whereas the true
crossover value is shown as BLUE x's, at treatment 21. In subplot 2, it can be seen that the true
86
closeness to crossover value is increasing over time, yet not quite to 1 which means that no
crossover has actually occurred. Also, the error starts out high (as expected) and then decreases
as a prediction comes closer to the actual crossover point.
Figure 21: Measurement 1 prediction after 10 observations
87
Figure 22: Measurement 1 prediction after 15 observations
Figure 23: Measurement 2 prediction after 10 observations
88
Figure 24: Measurement 2 prediction after 15 observations
Figure 25: Measurement 3 prediction after 10 observations
89
Figure 26: Measurement 3 prediction after 15 observations
Figure 27: Measurement 4 prediction after 10 observations
90
Figure 28: Measurement 4 prediction after 15 observations
At this point it is necessary to point out one important aspect of the modeling process.
Although predictions can be made every time an observation is added into the data set, if an
accurate crossover prediction is made for a certain measurement, then there is essentially no need
to continue adding in additional data. At that point, the information would be given to the
physician so that the patient could be re-evaluated for the need to construct a new facemask or
create a new treatment plan.
The analysis of the cross-sectional measurement data provided interesting results related
to which patients should have had a new facemask made and the ones which actually had a new
mask made during the course of treatment. Patients 11 and 17 were the only 2 patients who had
new masks made, while analysis of the data shows that approximately 15 of the patients
91
(including 11 and 17) actually needed some form of adaptive therapy, and likely a new
facemask. Figure 29 shows the fused images for Patient 17 right before a new mask was
constructed, and can be used to validate the results for this patient.
It can be seen in the figure that the areas of the patient's face that show anatomical
response correspond with the location with which Measurements 2 and 3 were taken. Going back
to Table 7, we can see that an accurate prediction was made for both measurements within 15
observations and slightly more for measurements 1 and 4. This means that after 15 observations,
the physician could have been notified that a change greater than 0.5 cm would occur around
treatment 25.
92
Figure 29: Illustration of anatomical changes for Patient 17 after 25 treatments
2 3
93
5 CONCLUSIONS
The focus of this dissertation was to use cross-sectional measurement data collected from
archived CT scans to create a tool for determining which patients will require and benefit from
adaptive radiation therapy, specifically for the purpose of creating a new facemask and/or a new
radiation therapy treatment plan.
It has been shown that many head and neck cancer patients undergo significant
anatomical changes which might make them a candidate for adaptive radiation therapy. The
facemask is an important part of ensuring accurate and reproducible radiation therapy treatments,
therefore it is imperative that ineffective masks be identified and replaced. Also, significant
changes in cross-sectional measurements could cause normal structures to shift into the treatment
field and result in escalated doses to health tissue. This could result in an inaccurate treatment
delivery; therefore a patient might require a new treatment plan along with a new facemask.
The task of identifying those patients who would benefit from ART was achieved by
collecting cross-sectional measurements of 30 patients' face and neck regions, and using the
newly developed two-part IGRT to identify those patients who would need a new mask and/or
treatment plan. Also, a time prediction was made as to when this was required so that the clinical
staff can allocate time and resources to prepare for adaptation of a patient’s treatment course.
The cross-sectional measurement data is truly an indicator for patients requiring new masks,
however they are also valuable in identifying those patients who might need a new treatment
plan with or without a new facemask.
94
Fifteen H&N patients were identified as ones who should have had a new facemask
constructed. The reality is that only 2 patients received new masks, and the delayed toxicities
experienced by the other patients could have possibly been prevented with the construction of a
new mask. The cross-sectional data proved to be an accurate representation of the anatomical
changes that each patient underwent. In other words, the predicted change in cross-sectional
measurement (for all 120 measurements) was used to correctly diagnose 74% of the
measurements that would require a patient to require adaptive therapy. Also, the prediction
results for those correctly diagnosed show that an average of 11 days are needed to ensure a
reliability of 0.5, while an even more reliable prediction can be made (reliability = 0.7) if 18
observations are initially supplied.
5.1 RECOMMENDATIONS FOR FUTURE WORK The focus of this dissertation was on the development of a tool which will aid the
oncologists in deciding which H&N patients will require adaptive radiation therapy. This is
needed because it will allow for the allocation of time and effort of the clinic staff to adequately
prepare for a new plan. It can also alert the oncologist as to which patients will require close
monitoring during their course of therapy.
In the future, the proposed methodology could be extended in the following directions:
- Develop a classification scheme by using rates of change of data and then further
develop the proposed modified kernel regression model to predict which patients
will require adaptive radiation therapy. This would be slightly different than the
95
work in this dissertation, in that one would actually attempt to make a prediction
based on historical patient data.
- Further evaluate the “abnormal” patients, who are the ones who have undergone
weight loss or tumor response. This will require the utilization of the rate of change
results. For example, if the rate of change is the same for all four lateral
measurements, it could indicate weight loss, whereas an asymmetric rate of change
might point to tumor response. The work done in this dissertation allows for
making the distinction between these two patients. However, the results are
deduced by visually evaluating the plots of the measurement data. More analysis
could involve tabular results and a more complex set of if/then rules.
- Integrate the IGRT tool into software programs that control IGRT machines,
specifically those with MVCBCT capabilities. It is known that MVCBCT is an
excellent imaging source for H&N patients because it can provide for increased
contrast between bone and soft tissue and decreased noise in its images. With these
types of images, the center of each vertebra could be automatically found and then
measurements could be taken by the radiation therapists while the patient is
undergoing treatment. This would provide the medical physicists with the data on a
weekly basis and then they could report to the oncologist as to what type of
anatomical changes (if any) the patient is undergoing.
96
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102
APPENDICES
103
APPENDIX A
104
A.1 ADDITIONAL DEMOGRAPHIC INFORMATION
This section includes weight loss information collected for each patient. It also reports
those patients who required the construction of a new facemask, indicated in YYYEEELLLLLLOOOWWW. The two
following figures show the data for those 2 patients.
Patient Weight Loss Information
Patient Number
Beginning Weight
(lbs)
Ending Weight
(lbs)
Total Weight Loss
Total Percent of Weight Lost
(%)
# of Elapsed Days at Ending Weight
Had new mask made
1 176 158 18 10.2 60 n 2 102.5 95 7.5 7.3 68 n 3 135.5 116 19.5 14.4 55 n 4 194 183.5 10.5 5.4 63 n 5 144 136 8 5.6 54 n 6 109 109 0 0.0 57 n 7 220 162.5 57.5 26.1 71 n 8 198.5 182 16.5 8.3 47 n 9 153 131.5 21.5 14.1 62 n
10 132.5 140 N/A N/A 43 n 11 194 176 18 9.3 74 y 12 218 202.5 15.5 7.1 48 n 13 152 146.5 5.5 3.6 62 n 14 196 168 28 14.3 65 n 15 212 192 20 9.4 62 n 16 179.5 150 29.5 16.4 62 n 17 298.5 261 37.5 12.6 60 y 18 171.5 168.5 3 1.7 47 n 19 58 70.5 N/A N/A 64 n 20 160.5 137 23.5 14.6 58 n 21 163.5 152 11.5 7.0 55 n 22 230 209 21 9.1 50 n 23 156 148 8 5.1 34 n 24 185 170.5 14.5 7.8 59 n 25 139.5 139.5 0 0.0 46 n 26 86.75 90.5 N/A N/A 55 n
105
27 211.5 202.5 9 4.3 63 n 28 232 215.75 16.25 7.0 63 n 29 193 176 17 8.8 65 n 30 N/A N/A N/A N/A N/A n
106
First of Two Patients to Have New Mask
5 10 15 20 25 30 355
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 355
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean Line
Crossover Line
5 10 15 20 25 30 355
5.5
6
6.5
7
7.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Trimmed Mean Line
Crossover Point
Patient 11
5 10 15 20 25 30 355
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
107
Second Patient to Have New Mask
5 10 15 20 25 30 35 40 455.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35 40 455.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean Line
Crossover Line
5 10 15 20 25 30 35 40 455.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Trimmed Mean Line
Crossover Point
Patient 17
5 10 15 20 25 30 35 40 455.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
108
APPENDIX B
109
B.1 MEASUREMENT DATA FOR ALL 30 PATIENTS
The first set of figures in this appendix are intended to illustrate how a reduction of the
dimensionality of the data was done for each patient and each measurement. To reiterate the
method, 4 different lateral measurements were taken at the C1-C4 vertebral levels and then each
measurement was taken out from its respective vertebra level. The result is having all of the
"ones", "twos","threes", and "fours" put together to create 4 different models for each patient.
The figures following these show which patients experienced anatomical changes that
caused their cross-sectional measurements to change by more than 0.5 cm, indicated by the
vertical lines and labelled "Measurement Crossover." The result from these measurement
changes indicates a need for a new facemask, however it was previously discussed that only 2
patients out of the 30 actually received a new mask.
110
111
112
113
114
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 3
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
115
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 3
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
116
5 10 15 20 25 30
4
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30
4
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30
4
5
6
7
8
9
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 4
5 10 15 20 25 30
4
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
117
5 10 15 20 25 30
4
5
6
7
8
9
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 4
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
118
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 5
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
119
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 5
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
120
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 6
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
121
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 6
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
122
5 10 15 20 25 30
4
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30
4
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30
4
5
6
7
8
9
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 7
5 10 15 20 25 30
4
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
123
5 10 15 20 25 30
4
5
6
7
8
9
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 7
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
124
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 8
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
125
5 10 15 20 25 30
5
5.5
6
6.5
7
7.5
8
8.5
9
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 8
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
126
5 10 15 20 25 30 35 40
4
5
6
7
8
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35 40
4
5
6
7
8
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35 40
4
5
6
7
8
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 9
5 10 15 20 25 30 35 40
4
5
6
7
8
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
127
5 10 15 20 25 30 35 40
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 9
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
128
5 10 15 20 25 30
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 10
5 10 15 20 25 30
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
129
5 10 15 20 25 30
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 10
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
130
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 11
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
131
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 11
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
132
5 10 15 20 25 30 35 40 45
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35 40 45
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35 40 45
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 12
5 10 15 20 25 30 35 40 45
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
133
5 10 15 20 25 30 35 40 45
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 12
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
134
5 10 15 20 25 30 35 40
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35 40
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35 40
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 13
5 10 15 20 25 30 35 40
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
135
5 10 15 20 25 30 35 40
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 13
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
136
5 10 15 20 25 30 354
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 354
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 354
5
6
7
8
9
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 14
5 10 15 20 25 30 354
5
6
7
8
9
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
137
5 10 15 20 25 30 35
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 14
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
138
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 15
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Treatment NumberC
ross
Se
ctio
na
l Me
asu
rme
nt(
cm)
Measurement Four
139
5 10 15 20 25 30 35
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 15
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
140
5 10 15 20 25 30 353
4
5
6
7
8
9
10
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement One
5 10 15 20 25 30 353
4
5
6
7
8
9
10
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Two
Trimmed Mean
Vertebra 1
Vertebra 2Vertebra 3
Vertebra 4
5 10 15 20 25 30 353
4
5
6
7
8
9
10
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Treatment Number
Measurement Three
Patient 16
5 10 15 20 25 30 353
4
5
6
7
8
9
10
Treatment Number
Cro
ss S
ect
ion
al M
ea
surm
en
t(cm
)
Measurement Four
141
5 10 15 20 25 30 35
3
4
5
6
7
8
9
10
Cro
ss S
ect
ion
al M
ea
sure
me
nt(
cm)
Treatment Number
Patient 16
Measurement One Crossover
Measurement Two Crossover
Measurement Three Crossover
Measurement Four Crossover
Measurement 1
Measurement 2Measurement 3
Measurement 4
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
Percent of Interpolated Data Used Modeling for Each Patient
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%
123456789
101112131415161718192021222324252627282930
Pat
ien
t N
um
ber
Percent of Data Interpoated
171
B.2 CONFERENCE PRESENTATION AND AWARD
Harris, C, A. Usynin, R. Seibert, and C. Ramsey (2008) “A Novel Technique for Predicting Which Head & Neck Patients will Require Adaptive Therapy”, Proceedings of the 50th Annual American Society of Radiologic Therapeutic Oncology (ASTRO) Conference, (72) pp.S84-85, 2008. This submission includes the preliminary results presented in this proposal. The presentation at the conference showed the methodology used for the initial developments of the IGRT tool. This submission was selected as the physics winner of the Resident Clinical Basic Science Research Award, which is the highest award given at this conference for medical physics residents. Once more work is done, it will be submitted to ASTRO’s journal (The Red Journal) for publication.
172
VITA
Carley Harris was born in Jackson, TN on November 1, 1982. She moved to Dickson, TN
before the beginning of high school and eventually graduated from Dickson County High School
in May 2001. She began attendance at the University of Tennessee, Knoxville in the summer of
2001 and graduated with her Bachelor of Science in Biomedical Engineering in May 2005 and
with her Masters of Science in Nuclear Engineering in May 2007.
As an undergraduate, she was a teaching assistant for the Freshman Engineering
Fundamentals Program (Engage) for 2 years and then would eventually become a graduate
teaching assistant for the Honors Section of the program. After entering the Nuclear Engineering
Department, she worked for the Engage program, took graduate classes, and volunteered at the
Thompson Cancer Survival Center for almost 2 years. It was at the cancer center where she
conducted research under the direction of Dr. Chester Ramsey, and would eventually become
employed as a Medical Physics Resident for 2 years.
She has completed and presented 5 conference publications as the primary author and has
co-authored 8 other conference publications.
While at the University of Tennessee, she was a member of the Pride of the Southland
Marching Band for 2 years, playing piccolo the first year and then baritone the second year. She
was also a member of Gamma Sigma Sigma National Service Sorority for 2 years.