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Prostate Cancer Treatment Planning: Sensitivity and Representative Objective Function Weights

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A Goli

A. Goli1,2*, J.J. Boutilier2, T. Craig3,4, M.B. Sharpe3,4 , T.C.Y. Chan2, (1) The University of Chicago Booth School of Business, Chicago IL, (2) Department of Mechanical and Industrial Engineering, University of Toronto, Toronto ON, (3) Department of Radiation Oncology, University of Toronto, Toronto ON, (4) Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto ON


WE-AB-209-11 (Wednesday, August 3, 2016) 7:30 AM - 9:30 AM Room: 209

Purpose: To develop an automated planning methodology that exploits patient sensitivity to objective function weights.

Methods: Given a treatment plan, we first create an acceptable treatment region that encompasses a set of treatment plans with similar clinical performance (e.g., +/-1% at V70Gy). We use inverse optimization to map this region in criterion space to the weight space and find a corresponding region of acceptable weight vectors (W). The shape and size of W describes how sensitive a patient is to perturbations in objective function weights. To exploit the information encoded by these regions, we approximate W for each patient by a polyhedron and we cluster patients using a novel integer programming model with cluster sizes from k=1,2,...,10. Each cluster centroid is a representative objective function weight vector and we use these weight vectors to generate k treatment plans for each patient (AUTO plans). Using 315 prostate cancer plans, we determine the number of patients that would have received an improved treatment plan using our automated approach.

Results: Clustering patients into five groups produced a global set of representative weights such that for 88% of patients there exists at least one AUTO plan that improves upon the clinical treatment plan in terms of organ-at-risk mean dose and clinical acceptability criteria satisfaction (i.e., V54Gy50% and V70Gy30%). The AUTO plans provided bladder or rectum mean dose improvement over the clinical treatment plans for 296 (94%) patients, bladder mean dose improvement for 185 (59%) patients, rectum mean dose improvement for 273 (87%) patients, and mean dose improvements for both bladder and rectum in 162 (51%) patients. The AUTO plans provided fewer violations for bladder/rectum V54Gy50% and slightly more for bladder/rectum V70Gy30% when compared to clinical plans.

Conclusion: A method combining inverse optimization and clustering automatically produces prostate treatment plans for 88% of patients.

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