The investigators study the use of optimization techniques in designing radiation treatment plans for cancer patients. In external-beam radiotherapy, beams of radiation are aimed into the patient's body from different angles. The treatment planning process chooses the shapes, intensities, and angles of the beams such that the cancerous tumor receives a high dose of radiation, while critical organs and normal tissue adjacent to the tumor are spared, to the extent possible. Radiation is not delivered in a single dose, but rather in fractionated daily doses over a period of up to 9 weeks. The latest generation of radiation devices allows the delivery of highly optimized and accurate treatment plans. This project uses the power of distributed computing and of advances in modeling and optimization to realize the full potential of these devices. Advances are being made on two major fronts. First, the quality of the initial treatment plan is improved by using stochastic and nonlinear optimization methodology to construct formulations of the treatment planning problem that take uncertainty explicitly into account. Second, imaging data gathered at each treatment session is used to perform adaptive radiotherapy, in which the treatment plan is adjusted between sessions to compensate for organ movement, tumor shrinkage, and other fractionation errors that occurred in previous treatment sessions. Handling of huge data sets and large quantities of compute cycles is needed to perform the required calculations in real time---in part, while the patient is lying on the couch of the treatment device.
Forty percent of all cancer patients receive radiation therapy as a key part of their treatment regimen, and devices that deliver radiation treatments are becoming progressively more sophisticated and powerful. This collaboration between mathematicians, computer scientists, and medical physicists is improving the effectiveness of the treatment planning process by choosing the shapes and intensities of the radiation beams used during treatments to target the cancerous regions more accurately and avoid radiation damage to healthy tissues. The optimization techniques being developed and implemented in the project also allow for day-to-day adjustment of the treatment plan, to account for changes in the internal organs and patient movement during treatment. The key feature of the project is its use of advanced computing platforms in conjunction with optimization methods to improve the effectiveness of a vitally important medical procedure.
