An Optimization Approach to Automate the Generation of Radiotherapy Treatment Plans

The main goal of radiotherapy is to deliver a specified dose of radiation directly to the tumor while minimizing radiation damage to healthy tissues. Currently, the treatment plan is being developed by professional planners using a commercial treatment planning system. In this treatment planning system, the planner modifies the objectives and weights of the objectives until an ideal combination of doses is achieved. This arbitrary process can cost a few hours or a day to finish. My research aims to automate the generation of the plans by implementing an optimization algorithm on top of the treatment planning system using gradient descent and other machine learning techniques. We have achieved good results for prostate cancer cases using the algorithm and will generalize the algorithm to apply to other cancer sites in the future.