ITR/AP: Collaborative Research: Cancer Treatment Plan Optimization

This project will use new techniques from computational optimization to design radiation therapy planning for cancer treatment. Radiation therapy applies ionizing radiation to cancerous tissue, damaging the DNA and interfering with the ability of the cancerous cells to grow and divide. This also damages healthy cells, but they are more able to repair the damage and return to normal function. The therapy planning problem is to specify the shapes of the applied radiation beams, times of exposure, etc., to deliver a specified dose to the tumor but not an excessive dose to the surrounding healthy tissue. New medical devices allow much control over the characteristics of the radiation, thus allowing much scope for the therapy planning. However, the full potential of these devices to deliver optimal treatment plans has yet to be realized due to the complexity of the treatment design process. By using advanced modeling techniques, state-of-the-art optimization algorithms, and implementations on parallel computing platforms this project will provide radiation oncologists with important new computational tools for treatment planning. These tools will be flexible enough to adapt to the varying priorities of different planners and different patients and robust enough to give good solutions to the most difficult planning problems. The project involves collaboration between three researchers whose collective expertise encompasses radiation oncology modeling, optimization algorithms, and parallel implementations. It builds on previous collaborations of these researchers on treatment planning and on NSF-funded work on algorithms for solving large optimization problems. The institutions involved in the project are the University of Wisconsin, the University of Maryland School of Medicine, and the University of Chicago.