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RTOG Bioinformatics

Y. Xiao, Ph.D.

RTOG, ACR

Radiation Oncology, Jefferson

Medical College

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Evidence Based

Radiation Oncology

Radiation Therapy Oncology Group (RTOG)

Improve The Survival Outcome And Quality Of Life

Evaluate New Forms Of Radiotherapy Delivery

Test New Systemic Therapies In Conjunction With Radiotherapy

Employ Translational Research Strategies

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RTOG Bioinformatics Mission

To facilitate the development and to

develop personalized predictive models

for radiation therapy guidance from

specific characteristics of patients and

treatments with integrated clinical trial

databases, bridging clinical science,

physics, biology, information technology

and mathematics

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BIOINFORMATICS ELEMENTS AND

PROCEDURES Available to BioWG

Database

RT Dose/Images/Clinical Data

Genomic/Proteomic

Biomarker

Data Analysis

Protocol development/Protocol operation

support/Trial Outcome-Secondary Analysis

Validation/Development/Research

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DATA/

DATA Integration

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RTOG DATA for BioWG Investigation Protocol N Endpoints

0022 oropharyngeal cancer 60 Salivary function

0117 lung 73 Pneumonitis and esophagitis

0126 prostate ~1500 Erectile dysfunction; rectal bleeding

Fecal incontinence vs dose

0225 nasopharyngeal 60 Salivary function

0232 prostate brachytherapy

0234 head and neck 230 TCP? Ongoing, not recruiting

0236 lung SBRT 52 Ongoing: TCP, toxicity

0321 prostate HDR brachy 110 Late/Acute GU/GI

0522 head and neck Local control

0529 IMRT anal canal cancer 59 GI/GU acute

9311 lung ~150 NIH R01 (Deasy). Toxicity: esophagitis; pneumonitis

9406 EBRT prostate 800 NIH R01 (Tucker) toxicity

9803 3D CRT GBM 40 Brain toxicity

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Rapid Learning

CAT (Computer Assisted Theragnostics)

MAASTRO/RTOG Collaboration

Andre Dekker, PhD

MAASTRO Knowledge Engineering

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Why Rapid Learning/CAT?

[..] rapid learning [..] where we can learn from each patient to guide

practice, is [..] crucial to guide rational health policy and to contain costs

[..].

Lancet Oncol 2011;12:933

Personalized medicine

• Explosion of data

• Explosion of decisions

• Decision support

• Evidence base

Personalized medicine improves survival and quality of life.

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Prediction by MDs?

• Non Small Cell Lung

Cancer

• 2 year survival

• 30 patients

• 8 MDs

• AUC: 0.57

• Retrospective

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How to get

data for rapid learning

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Challenges to share data

[..] the problem is not really technical […]. Rather, the problems are

ethical, political, and administrative. Lancet Oncol 2011;12:933

1.Administrative (time)

2.Political (value, authorship)

3.Ethical (privacy)

4.Technical

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CAT approach

CAT is a research project in which

we develop an IT infrastructure -> technical

to make radiotherapy centers

semantic interoperable (SIOp*) -> administrative

while the data stays inside your hospital -> ethical

under your full control -> political

* SIOp level 3 = Machine Readable ->Data in common syntax and with common meaning

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Components

ETL

Deident. & Filter

Ontology

XML DICOM

Application Sharing

Distributed Learning

CTMS PACS

Export

Query

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Key features

• No sharing of data, truly federated

• Machine learning (retro.) & clinical trials (prosp.)

• NCI Thesaurus with formal additions

• 5 languages, 5 countries & 5 legal systems

• Focus on radiotherapy

• Inclusion of non-academic centers

• Industry involvement

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Network 11/2011

Active or funded CAT partners (10)

Prospective centers (4)

2

5

Map from cgadvertising.com

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Laryngeal carcinoma model

• 994 MAASTRO patients

• 1990-2005

• www.predictcancer.org

• Input parameters

– Age

– Hemoglobin

– T-stage

– EDQ2T (Gy)

– Gender

– N+

– Tumor location

• Output parameters

– Overall survival

www.predictcancer.org, Egelmeer et al., Radiother Oncol. 2011 Jul;100(1):108

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Larynx Query

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Validation - Result

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Distributed

Learning

Architecture

Update

Model

Learn Model

from Local Data

Central Server

Model Server

RTOG

Send Model Parameters

Final Model Created

Learn Model

from Local Data

Learn Model

from Local Data

Model Server

MAASTRO

Model Server

Roma

Send Model Parameters

Send Model Parameters

Send Average Consensus Model

Send Average Consensus Model

Send Average Consensus Model

Only aggregate data is exchanged between the Central Server and the local Servers

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Distributed Learning Results

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Web-based Documentation

System

with Exchange of DICOM RT for

Multicenter Clinical Studies

in Particle Therapy

Priv.-Doz. Dr. med. Stephanie E. Combs, DEGRO 2012

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PARTICLE THERAPY

• particle therapy is a new and promising technique in radiation

oncology for cancer treatment

• compared to standard radiation therapy (RT) with photons, it is with

either

– ions (e.g. Carbon C12, Helium He2, Oxygen O16) or

– protons (H1)

• advantages:

− more precise dose delivery

to the target

sparing of normal tissue and

organs at risk

− enhanced radiobiological

effectiveness (carbon ions) increase in local tumor control

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• First results of clinical phase I and II trials performed at NIRS support the assumption that carbon ions provide an enhanced biological effectiveness in adenoid cystic carcinomas, H/N melanomas, lung and liver tumors, large soft tissue sarcomas, chordomas / chondrosarcomas and prostate cancer

• Randomized trials proving the superiority of heavy charged particles in comparison to photon IMRT and protons required – Randomized trial of proton vs. Carbon ion RT for chordomas and

chondrosarcomas of the skull base

• Radiobiologic research will enable better exploitation of the advantages of carbon ion RT in future trials

• Physics Research: e.g.High Precision Treatment of Moving Targets

What we need to do.....

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HIT

HEIDELBERG ION-BEAM THERAPY

CENTER

• began patient treatment in Nov. 2009

• main focus:

– clinical studies to evaluate the benefits of ion therapy for several indications

• ULICE project

(Union of Light Ions Centers in Europe)

– development of a database with transnational access

– platform for international clinical multicenter studies

– accessible by external/internal oncologists, physicists, researchers

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Establish a common database for hadrontherapy.

- few centers have treated patients with particle therapy

- heterogeneous concepts of dose prescription, delivery

- hetereogeneous studies and treatment protocols

Exchange of

clinical

experience

Harmonized

study and

treatment

concept

Transfer of

know-how

Standards and

basic (biologic)

data ULIC

E D

ATA

BA

SE

ULIC

E D

ATA

BA

SE

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METHODS

• Approach:

– central, web-based system

– interfaces to the mandatory existing information systems of the hospital

– avoidance of redundant entry of any patient/clinical data

– study-specific modules, which can easily be adapted

– implementation of security and data protection measures to fulfill legal

requirements

Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

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GENERAL PRINCIPLES AND ARCHITECTURE

• database with standard interfaces of the PACS world

– storage of all data in an SQL database (DICOM data model)

– possibility to dynamically extend with additional data structures

– interfaces to process and import information from

• DICOM data

• HL7 messages

– Java applet for

• manual import of data by web-upload

• receiving and sending data with DICOM C-Store

– the underlying components are compliant with the IHE Framework

• telemedicine record

– combines the functionality of

• a medical record with

• a professional DICOM RT viewer (Class IIb)

– allows us to exchange, store, process and visualize text data, all types of

DICOM images, and any other multimedia data

– with extensions for study documentation

Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

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INTEGRATION OF OTHER INFORMATION

SYSTEMS

Kessel K., ..., Combs SE, Radiat Oncol

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SECURITY CONCEPT

• the system uses the encrypted https protocol to exchange data between

the central server and other systems

• users need an account and password to access the system

• a roles-and-rights concept enables a very detailed configuration for each

study or client

• (basic patient) data can be pseudonymized

– the original patient information is kept in a separate database

– when a user has the right to view all data of a patient, the data is de-

pseudonymized “on the fly”

• external users

– communication via application gateway in the demilitarized zone (DMZ)

– client certificates to verify authorized browsers

Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

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RESULTS

• documentation began May 2011 and we documented 900 patients

• all access to the system takes place over the Internet

– no software needs to be installed (except a JRE)

• exchange and storage of various DICOM RT data

• visualization with the DICOM RT ion viewer

– examination and review of radiation plans from every computer in the

hospital without having to go to a PACS or a radiation therapy planning

station

• with our web-based approach, we have succeeded in developing a

database for multicenter studies within the ULICE project

• future prospects: extend automatic and electronic study analyses

Kessel K., ..., Combs SE, Radiat Oncol, 7, 115 (2012).

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More to Integrate

Andre Dekker, PhD

MAASTRO Knowledge Engineering

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How to learn better?

• AUC of 0.85 is needed

• Survival model AUC 0.75

• More patients

• More variables

• Diversity

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More variables from a simple CT

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Biomarkers

General

Radiomics

(n = 150)

LOO-AUC 0.75

Selected image features: CT Range,

Cluster Shade, Run Percentage

LOO-AUC 0.76

Selected biomarkers: IL6, IL8, CEA

LOO-AUC = 0.72:

Gender, WHO, FEV1, Positive lymph

nodes,

Tumor Volume

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Biomarker: IL6, IL8, CEA

General: Gender, WHO-PS, FEV1, Positive lymph nodes, Tumor Volume

Radiomics: Range, Run Length, Run Percentage

General Biomarkers

n = 131

Radiomics

AUC 0.87

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DATA ANALYSIS

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DATA ANALYSIS

- Evidence Based Radiation

Therapy Quality Assurance

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DATA ANALYSIS

- Evidence Based Radiation

Therapy Quality Assurance

(Structure Definition)

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Critical Impact of Radiotherapy

Protocol Compliance and

Quality TROG 02.02

L. J. Peters et al, Journal of Clinical Oncology, vol. 28, Number 18, June 2010

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Failure To Adhere To Protocol

Associated With Decreased

Survival: RTOG 9704

R. Abrams et al, Int. J. Radiation Oncology Biol. Phys., Vol. 82, No. 2, pp. 809–816, 2012

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Target Defined from Multiple

Institutions

S. Kong, Y. Xiao, M. Machtay, et. Al., A “Dry-Run” Study for RTOG1106/ACRIN6697: A Randomized Phase

II Trial of Using During-Treatment FDG-PET and Modern Technology to Individualize Adaptive Radiation

Therapy in Stage III NSCLC ,IASLC, 2011

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Target Inter-Observer

Variability

Pre-GTV Statistics

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OAR Variation

Th

e maxim

um

volu

me o

f

bra

ch (b

rach

ial p

lexu

s) is up

to 4

-5 fo

ld o

f the m

inim

um

.

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Dry Run for Segmentation and

Plan Evaluation – 1106 Example

GTV contours from different institutions. Red thick line represents the consensus contour.

(a) Case1, (b) Case2.

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Oral Presentation AAPM 2012

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RTOG 0617, NCCTG N0628,CALGB 30609

Conventional vs. High Dose RT

R

A

N

D

O

M

I

Z

E

RT: 60 Gy

Paclitaxel

Carboplatin +/-

Cetuximab

RT: 74 Gy

Paclitaxel

Carboplatin +/-

Cetuximab

Paclitaxel

Carboplatin X 2

+/- Cetuximab

J. Bradley et al, ASTRO 2011

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Overall Survival

Ove

rall

Su

rviv

al (%

)

0

25

50

75

100

Months since Randomization

0 3 6 9 12

*One-sided p-value, left tail

Patients at Risk60 Gy74 Gy

213204

190175

149137

124116

104 93

Dead

5870

Total

213204

HR=1.45 (1.02, 2.05) p*=0.02

60 Gy74 Gy

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DATA ANALYSIS

- Evidence Based Radiation

Therapy Quality Assurance

(Image Guided Radiotherapy)

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IGRT Data Submission

Components

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Variations Between Systems

Y. Cui (Xiao) et al, Int. J. Radiation Oncology Biol. Phys., Vol. 81, No. 1, pp. 305–312, 2011

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IGRT Credentialing for RTOG

Protocols

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IGRT Variations

Y. Cui (Xiao) et al, Implementation of Remote 3D IGRT QA for RTOG Clinical Trials,

Int. J. Radiation Oncology Biol. Phys., In Press

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DATA ANALYSIS

- Evidence Based Radiation

Therapy Quality Assurance

(Intensity Modulated

Radiotherapy Planning)

DUKE University Radiation Oncology

Establishing knowledge-based

models for IMRT planning

quality evaluation

Q. Jackie Wu

Yaorong Ge

Jan 2012

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Case1 Right Parotid Case 2 Left Parotid

0 20 40 60 80 1000

20

40

60

80

100

Percent dose (%)

Perc

ent volu

me (

%)

Case 1 R Parotid Case 2 L Parotid

Parotid DVH

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Knowledge Models For IMRT

Planning QA

• Understand the patient anatomical, physiological

factors that influence dose coverage

• Quantify their individual influence via mathematical

modeling and machine learning

• Codify treatment planning experience and guidelines

using knowledge engineering

• Model Use these factors to model the inter-patient

variation and to provide “peer-review” type guidance

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0 50 100 0

50

100

Case 1

50 100

Case 2

50 100

Case 3

50 100

Case 4

50 100

Case 5

50 100

Case 6

0 50 100 0

50

100

Case 7

50 100

Case 8

50 100

Case 9

50 100

Case 10

50 100

Case 11

50 100

Case 12

0 50 100 0

50

100

Case 13

50 100

Case 14

50 100

Case 15

50 100

Case 16

50 100

Case 17

50 100

Case 18

0 50 100 0

50

100

Case 19

50 100

Case 20

50 100

Case 21

50 100

Case 22

50 100

Case 23

50 100

Case 24

Example Of Parotid Sparing

Modeling

Vo

lum

e (

%)

Dose (%)

Vo

lum

e (

%)

Vo

lum

e (

%)

Vo

lum

e (

%)

Dose (%)

Dose (%)

Dose (%)

Actual Plan DVH

Modeled Lower

and Upper

Bound of DVH

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Moving Forward

• Bigger Database

– Knowledge and experience embedded in the IMRT

cases

– Collaboration with RTOG who has large case pools

• Comprehensive Knowledge Base

– All types of knowledge sources

– Predictive models

– Ontological framework

• Intuitive and Integrated Decision Support

– Minimize cognitive burden

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Intuitive and Integrative Decision

Support

Prediction QUANTEC Guidelines

Gui

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Experience-based dose volume

histogram prediction in IMRT: A new

QC paradigm

Kevin L. Moore, Ph.D., DABR

61 KL Moore et al, "Experience-based QC of IMRT planning" IJROBP 82, 545 (2011)

Introduction

• Current TPSs cannot guarantee that IMRT plans are fully optimal, and applying quality

control methods to the treatment planning process can have a very large impact

• The degree to which plan quality variations compromise patient care is unknown

• Investigations (WashU, JHU, ROR) have shown that plan quality variations can be

quite sizeable, vastly exceeding other uncertainties and putting patients that should

have had a low risk of complications into a high-risk category

• Quantitative assessment of PTV metrics is easy, normal tissue sparing is not.

Anatomical variations confound the plan evaluation process because expected values

are (as yet) not available.

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Predicting DVHs with universal basis function evolution

• With universal fitting function and parameter fits pjq(k), which are derived from all N

training patients, we have the desired result:

• This equation takes as input geometric variables and the derived fitting parameters

and outputs a predicted DVH for an organ based only on the input structure set SSij

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Results: rectum and bladder predictions (training pts)

N=20 training patients

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Results: rectum and bladder predictions (“new” pts)

M=20 validation patients

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Quantifying plan quality variations in RTOG trials

• These data represent an unique and nearly ideal test bed to not only quantify plan

quality variability but, due to the associated outcomes data with the treatment plans, it

can address its clinical importance as well

• Employ modeling toolkit as

secondary study on closed

RTOG protocol (e.g. 01-26) to

quantify the clinical

importance of IMRT plan

variability

• Plan quality variations in a large-scale, multi-

institutional trial will serve as an excellent

surrogate for the variability in plan quality

across IMRT practitioners

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DATA ANALYSIS

- Analytical Algorithms

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Statistical inference

Frequentist inference Bayesian inferenceProbability: the proportion of times that an event would occur in a large number of similar repeated trials.

Model parameters are fixed, use the observed data to make Inference about parameters, e.g. Maximum Likelihood Estimation, confidence intervals and P-values

Probability describes degree of belief. It reflects one’s strength of belief that the proposition is true. Bayesian inference inherently embraces a subjective notion of probability.

Start with a prior belief about the likely values of model parameters, then use observed data to modify these parameters, i.e., deriving posterior probability distribution.

The Dempster-Shafer theory is an extension of the Bayesian inference.

Wenzhou Chen, Yunfeng Cui, Yanyan He, Yan Yu, James Galvin, Yousuff M. Hussaini, Ying Xiao, “Application of Dempster-Shafer Theory in Dose Response Outcome Analysis”, Physics in Medicine and Biology, In Press

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Probability, Belief & Plausibility of

RP

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LKB parameters from Dempster–

Shafer theory and other references

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FUTURE DIRECTIONS

Introducing A New Organizational Structure

NCI Clinical Trials Network

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Institution Patient data

ACR

Cloud IROC

QA Medidata

Rave

Study Group

Patient data

IROC

Study Groups (second analysis, outcomes,

publications etc.)

QA

QA

Imaging

Studies

Data and QA Process Flow

(receipt, QA, storage)

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