|Institution:||University of Washington|
|Keywords:||Adaptive treatment planning; Convex; Dynamic optimization; IMRT; Radiation therapy; Robust optimization; Industrial engineering; Operations research; Oncology; Industrial engineering|
|Full text PDF:||http://hdl.handle.net/1773/40917|
In external beam radiotherapy for cancer, high-energy radiation is passed through the pa- tients body from an outside source to kill tumor cells. The challenge is that radiation also damages healthy tissue and organs-at-risk (OAR) in its path. The objective therefore is to devise treatment plans that maximize tumor-damage while protecting healthy anatomies. Treatment planners attempt two separate methods to attain this goal: spatial and biological. The spatial side focuses on the geometry and physics of the problem. The key consider- ation here is the location of the tumor relative to the nearby healthy regions as seen in an anatomical image, and the dose (energy absorbed per unit mass) deposition properties of the radiation beam. The treatment planner prescribes a high dose to the tumor and puts upper limits on the doses delivered to the healthy regions. Intensity Modulated Radiation Therapy (IMRT) technology is then employed to tune the profile (fluence-map) of the radiation beam to administer a dose that is as close as possible to this tumor-conforming prescription. Sev- eral mathematical optimization models and solution algorithms for this problem have been developed and embedded into treatment planning systems. The biological side of planning exploits the difference between the dose-response charac- teristics of tumors and healthy anatomies. For example, healthy cells are believed to possess better damage repair capabilities than tumor cells. Thus, treatment is delivered over mul- tiple sessions to give healthy tissue some time to recover between sessions. This is called fractionation. Fractionation also gives the tumor some time to re-oxygenate, which increases its sensitivity to radiation. Tumors, however, proliferate during the treatment course, and hence, too long a treatment course may not be ideal. One key question on this biological side is to determine the optimal number of treatment sessions. This is called the fraction- ation problem. Existing optimization research on the fractionation problem relies on the linear-quadratic (LQ) model of dose-response with tumor- and OAR-specific parameters to approximately capture the behavior of the complex biological system involved. Recent studies have suggested that an integrated approach that simultaneously tackles the spatial and biological sides of the problem may lead to a higher tumor-damage as com- pared to tackling the two aspects separately. The goal in such integrated formulations is to simultaneously find the fluence-map and the number of sessions that maximize tumor- damage while limiting toxic effects of dose on the healthy anatomies. Emerging advances in quantitative functional imaging technologies are enabling planners to observe the tumors actual dose-response over the treatment course. This provides additional opportunities for better-utilizing the LQ model by dynamically adapting treatment plans to further improve outcomes. The challenge, however, is that spatiobiologically integrated formulations based on the LQ model typically yield nonconvexAdvisors/Committee Members: Ghate, Archis (advisor).