Parameter selection in prostate IMRTRenzhi Lu, Richard J. Radke1 , Andrew Jackson2

Rensselaer Polytechnic Institute1,Memorial Sloan-Kettering Cancer Center2

Abstract Current intensity modulated radiotherapy (IMRT) planning arrives at intensity plans via optimization algorithms that make compromises between competing clinical objectives. However, such compromises are not easy to specify in terms of the parameters defining the IMRT objective function. Planners currently adjust the optimization parameters in trial-and-error way to get a clinically acceptable plan. To circumvent this procedure in prostate IMRT, we present a novel approach that can automatically produce optimization parameters that will meet the clinical objectives given a contoured CT scan of the patient. Based on training data, we identified key combinations of parameters to which the IMRT optimization is sensitive. By searching in this reduced parameter space, we find the combination of the important parameters that lead to the 'best' plan. The starting point of the search is obtained from machine learning, i.e., we learn the relationship between these parameters and the patient's geometry from training data. Our initial experiments indicate that we can automatically determine the plan parameters that satisfy the clinical constraints within 5 minutes, a task that may take experienced human planner several hours.

State-of-the-art• Hunt et al[1] gave specified procedure for changes to be made in optimization parameters given specific deficits in plans.• Radke et al[2] built active shape model and appearance model for prostate, bladder and rectum in prostate IMRT.

Challenges and significanceThe bottleneck of current IMRT system is not optimization, but the back-and-forth procedure for adjusting optimization parameters. Circumventing or minimizing this procedure would save many person-hours of effort. However, high-dimensionality of parameter space and significant degeneracy make the problem difficult.

Technical approach1. Problem description

Fig. 1 Prostate IMRT: visualization of beams and structures

Input : Settings for 5 beams. Contours for 6 structures. Optimization : .Given a parameter setting P ,find the best radiation intensities I.Adjustment : Change the parameters P , redo optimization.Objective : Dose D in target is as prescribed, in normal tissues is minimized.

7. Result

Fig.4 Dose-Volume histogram of PTV(prostate), rectum and bladder for 3 cases.

Table.1 Dose statistics for 3 cases, whether they satisfy the clinical requirement. Case 1: IMRT From default settings of parameters, PTV constraints are

not satisfied. Case 2: Numerical optimization over sensitive parameter set using

random starting point. Again,some constraints are not satisfied. Case 3: Optimization over sensitive parameter set using the machine

learning result as the starting point. We get the best PTV coverage while avoiding PTV hotspot. It also satisfies the clinical constraints for rectum and bladder. Thus a good starting point from machine learning ensure the clinical goodness of the final plan.

Accomplishments up through current year• Predict breast IMRT plan by learning the relationship between geometry

and intensities, submitted to IEEE Trans. on Biomedical Engineering.• Automatically generate the parameters for prostate IMRT by machine

learning and direct search.

Future Plans• Improve the geometric model in IMRT, and try to predict the intensity

directly from the model. • Extend the approach to head-and-neck IMRT.

References1. Hunt et al, “Evaluation of concave dose distributions created using an

inverse planning system,” Int J Radiat Oncol Biol Phys, Vol 54, pp. 953-62, 2002.

2. Freedman, Radke et al ,“Model-based segmentation of medical imagery by matching distributions,” IEEE Trans. on Medical Imaging, 24(3):281--292, March 2005.

Contact info.Richard J. Radke, Assistant professorDept. of Electrical, Computer, and Systems EngineeringRensselaer Polytechnic Institute110 8th Street, Troy, NY 12180phone: (518)276-6483, e-mail: rjradke@ecse.rpi.edu

This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821).

2. System overview

Fig. 2 Overview of our approach

3. Score function for parameter global optimization Multiple clinical rules are incorporated into a single score function to evaluate the plan from a given parameter set. Rule 1: PTV max dose<111. Rule 2: Rectum max dose<99,

… Rule i: …Overall Score:

4. Dimensionality reduction in parameter space

We identify 6 important parameters which most affect the resultant dose evaluation. Search in this combination of sensitive set instead of the whole parameter space.

Fig. 3 Examples of sensitive set. PTV V95(left) and PTV Dmax(right) as a function of the PTV Dmax weight and Dmin weight. The relationship is also controlled by Rectum max dose, Rectum DVH dose and Bladder max dose.

5. Direct search algorithm for parameters • Pattern search • Powell method • Simplex search

6. Machine learning for initialization 6.1 Geometric modeling of structures: Re-sample the contours and build PCA model for joint structures.

Fig. 4 Examples of contour re-sampling and PCA modeling 6.2 Machine learning for parameter set : Input features: Weights of PCA model for a given geometry Output : Parameters for IMRT optimization

)|)((minarg PIDFI

...)()()( maxRemax ctPTV DfDfDR