Defense Date


Document Type


Degree Name

Doctor of Philosophy


Medical Physics

First Advisor

Geoffrey Hugo

Second Advisor

Jeffrey Siebers


Prognosis for lung cancer patients remains poor. For those receiving radiation therapy, local control and survival have been shown to improve with increased doses; however, deliverable dose is often limited by associated toxicity. Therefore, methods that reduce dose to normal tissues and allow isotoxic escalation are desirable. Adaptive radiation therapy seeks to improve treatment by modifying the initial plan throughout delivery, and has been shown to decrease normal tissue dose. Studies to date suggest a trend of increasing benefit with increases in replanning frequency; however, replanning is costly in terms of workload and past studies implement at most weekly adaptation. The purpose of this thesis is to quantify the benefit associated with daily replanning and characterize the tradeoff between replanning frequency and adaptive benefit. A software tool is developed to facilitate planning studies and to introduce complimentary methods for evaluating adaptive treatments. Synthetic images and contours are xii generated for each fraction of a typical fractionation schedule using principal component analysis and a novel method of sampling coefficients that preserves temporal trends in the data (e.g. tumor regression). Using the synthetic datasets, a series of adaptive schedules ranging from no adaption to daily replanning are simulated and compared to quantify adaptive benefits and characterize tradeoffs with frequency. Daily replanning resulted in significant reductions in all normal tissue planning metrics when compared to no adaptation, and incremental reductions were observed with each increase in replanning frequency while the magnitude of average reductions decreased with each step. Modest correlation between absolute change in planning target volume over the course of treatment and reductions in both mean lung dose and mean esophageal dose were observed.


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