A previous approach to robust intensity-modulated radiation therapy (IMRT) treatment planning for moving tumours in the lung involves solving a single planning problem before treatment and using the resulting solution in all of the subsequent treatment sessions. In this thesis, we develop two adaptive robust IMRT optimization approaches for lung cancer, which involve using information gathered in prior treatment sessions to guide the reoptimization of the treatment for the next session. The first method is based on updating an estimate of the uncertain effect, while the second is based on additionally updating the dose requirements to account for prior errors in dose. We present computational results using real patient data for both methods and an asymptotic analysis for the first method. Through these results, we show that both methods lead to improvements in the final dose distribution over the traditional robust approach, but differ greatly in their daily dose performance.ii
AcknowledgementsFirst of all, I would like to thank my advisor and friend, Timothy Chan. I cannot begin to express my gratitude for his thoughtfulness, patience, encouragement and support over the last two years. One of the things that I have learned about being a researcher is that you get a front-row seat to the full spectrum of human emotion: the dizzying highs that come with an exciting new theoretical result or computational result, but also the dark lows when you just cannot push that proof any further or your computational experiments do not work out as you had hoped they would. Whenever