Poster - Thur Eve - 69: Computational Study of DVH-guided Cancer Treatment Planning Optimization Methods

Abstract

Purpose: To compare methods to incorporate the Dose Volume Histogram (DVH) curves into the treatment planning optimization. Method: The performance of three methods, namely, the conventional Mixed Integer Programming (MIP) model, a convex moment‐based constrained optimization approach, and an unconstrained convex moment‐based penalty approach, is compared using anonymized data of a prostate cancer patient. Three plans we generated using the corresponding optimization models. Four Organs at Risk (OARs) and one Tumor were involved in the treatment planning. The OARs and Tumor were discretized into total of 50,221 voxels. The number of beamlets was 943. We used commercially available optimization software Gurobi and Matlab to solve the models. Plan comparison was done by recording the model runtime followed by visual inspection of the resulting dose volume histograms. Conclusion: We demonstrate the effectiveness of the moment‐based approaches to replicate the set of prescribed DVH curves. The unconstrained convex moment‐based penalty approach is concluded to have the greatest potential to reduce the computational effort and holds a promise of substantial computational speed up.