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An Evaluation of Three Robust Optimization Approaches in IMPT Treatment Planning

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W Cao

W Cao1*, M Zaghian2 , W Liu3 , L Kardar4 , S Randeniya5 , G Lim6 , R Mohan7 , (1) UT MD Anderson Cancer Center, Houston, TX, (2) University of Houston, Houston, TX, (3) Mayo Clinic Arizona, Phoenix, AZ, (4) University of Houston, Houston, TX, (5) UT MD Anderson Cancer Center, Houston, TX, (6) University of Houston, Houston, TX, (7) UT MD Anderson Cancer Center, Houston, TX


TH-C-BRD-10 Thursday 10:15AM - 12:15PM Room: Ballroom D

Various robust optimization approaches have been proposed to ensure the robustness of intensity modulated proton therapy (IMPT) in the face of uncertainty. In this study, we aim to investigate the performance of three classes of robust optimization approaches regarding plan optimality and robustness.

Three robust optimization models were implemented in our in-house IMPT treatment planning system: 1) L2 optimization based on worst-case dose; 2) L2 optimization based on min-max objective; and 3) L1 optimization with constraints on all uncertain doses. The first model was solved by a L-BFGS algorithm; the second was solved by a gradient projection algorithm; and the third was solved by an interior point method. One nominal scenario and eight maximum uncertainty scenarios (proton range over and under 3.5%, and setup error of 5 mm for x, y, z directions) were considered in optimization. Dosimetric measurements of optimized plans from the three approaches were compared for four prostate cancer patients retrospectively selected at our institution.

For the nominal scenario, all three optimization approaches yielded the same coverage to the clinical treatment volume (CTV) and the L2 worst-case approach demonstrated better rectum and bladder sparing than others. For the uncertainty scenarios, the L1 approach resulted in the most robust CTV coverage against uncertainties, while the plans from L2 worst-case were less robust than others. In addition, we observed that the number of scanning spots with positive MUs from the L2 approaches was approximately twice as many as that from the L1 approach. This indicates that L1 optimization may lead to more efficient IMPT delivery.

Our study indicated that the L1 approach best conserved the target coverage in the face of uncertainty but its resulting OAR sparing was slightly inferior to other two approaches.

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