Using inverse optimization to improve radiation therapy treatments for prostate cancer
Intensity-modulated radiation therapy (IMRT) is an advanced cancer treatment technology that uses beams of high energy x-rays to deliver radiation to a tumour. In IMRT, radiation beams are divided into many small beamlets, whose intensities are individually adjusted in order to deliver a dose of radiation that conforms to the shape of the tumour. The intensities of each radiation beamlet are computed using specialized software. In this software, optimization is the primary technique driving the computations. That is, the treatment planning problem is modeled as a mathematical optimization problem, and then solved using mathematical algorithms. In this project, we will design and test an optimization methodology aimed at determining weights for the IMRT treatment planning problem in an automated and objective manner. The specific optimization methodology we will use is called “inverse optimization”. The standard or “forward” problem involves choosing weights, and solving the optimization problem in order to come up with a treatment. In inverse optimization, we input the treatment, and the optimization gives us the weights. We will use treatments of prostate cancer patients from Princess Margaret Hospital (PMH), one of the largest cancer hospitals in the world (and based right next to the University of Toronto campus), as input to our optimization models.
We expect this research to lead to an improved understanding of the “best” weights to use in treatment planning for prostate cancer. We intend to show that by using optimized weights in treatment planning, we can design treatments that are almost identical to clinically designed treatments, without following a time-consuming trial and error process. Ultimately, we hope to improve the treatment planning process by providing increased automation where possible. We will also study the effects of different anatomical geometries on the determination of optimized weights for different patient classes.
The Globalink student will play a significant role in this project. S/he will be part of a team consisting of a PhD student, researchers at PMH, and myself. S/he will be involved with: 1) analyzing historical treatment plans and preparing data for optimization; 2) running optimization models; 3) analyzing results from optimization.
