Optimal adaptation of radiation therapy treatments

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. The intensities of each radiation beamlet are computed using specialized software. In this software, the treatment planning problem is modeled as a mathematical optimization problem, and solved using mathematical algorithms.

Treatments need to account for potential uncertainties that may degrade treatment quality. For example, tumours in the lung move as the patient breathes, so when solving the mathematical optimization problem to design a radiation therapy treatment, such motion must be accounted for. My research group has designed novel robust optimization methods to optimize radiation therapy treatments subject to such uncertainties. Furthermore, we have developed adaptive methods that allow the treatment to be adapted to patient changes as the treatment progresses – treatments are normally spread over multiple weeks.

This Globalink project will build on existing research that is being conducted in my research group on adaptive and robust radiation therapy. In particular, there are two related problems that will be explored in this project. First, the question is “how often should a treatment be adapted”? Our initial research shows that treatments that adapt to uncertainty perform better than those that don’t, but the question of how often to adapt is still open. Frequent adaption likely leads to better clinical results, but ends up being quite costly for the hospital. To address the first question, we will start with an empirical analysis using historical patient data and exhaustive search to determine optimal treatment adaption times (retrospectively), given a budget of one adaption, two adaptions, etc. That is, if we are allowed to re-optimize the treatment once to adapt to observations of the uncertainty, when should we re-optimize. Guided by the empirical findings, we will develop a mathematical model that can be applied to future treatment cases to determine guidelines on treatment adaptation.

The second problem will focus on extending our previously developed mathematical models for adapting a treatment to consider incorporating previous dose information in a novel way. We have developed preliminary adaptive optimization models that account for previous dose information (e.g., if certain parts of the tumour are underdosed, then the model will focus on increasing dose to those regions in subsequent treatment days), but they require more extensive testing. We have hypothesized additional enhancements to the model that can more accurately measure previous dose delivered and adjust future dose requirements. We will implement these new algorithmic ideas, incorporate them into our previously developed models and test whether dose results are improved with the new model.

Faculty Supervisor:

Timothy Chan







University of Toronto


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