Radiation therapy is an important modality in cancer treatment. To find a good treatment plan, optimization models and methods are typically used, while dose-volume requirements play an important role in plan's quality evaluation.

We compare four different optimization approaches to incorporate the so-called dose-volume constraints into the fluence map optimization problem for intensity modulated radiotherapy. Namely, we investigate (1) conventional Mixed Integer Programming (MIP) approach, (2) Linear Programming (LP) approach to partial volume constraints, (3) Constrained Convex Moment (CCM) approach, and (4) Unconstrained Convex Moment Penalty (UCMP) approach. The performance of the respective optimization models is assessed using anonymized data corresponding to eight previously treated prostate cancer patients. Several benchmarks are compared, with the goal to evaluate the relative effectiveness of each method to quickly generate a good initial plan, with emphasis on conformity to DVH-type constraints, suitable for further, possibly manual, improvement.

This is joint work with Tuan Tran and Quentin Shao. This work was supported by the NSERC Discovery Grant program.