Towards Adaptive Robust Radiotherapy to Manage Radioresistance

Abstract

In radiotherapy, uncertainties in tumor radioresistance and its progression can degrade the efficacy of deterministic treatments. While a robust methodology can overcome this, it often produces overly conservative or suboptimal decisions, especially when there are changes in time. We aim to develop an adaptive radiotherapy planning framework that can reduce over-conservatism yet remain robust to the uncertainties in radioresistance. Specifically, intermediate imaging is used to update the uncertainty at each stage and curb over-conservatism. While additional imaging reduces uncertainty, it accrues costs such as extra radiation to organs, which deters continuous imaging. We probe this trade-off in uncertainty and cost of observation by computing and comparing results from two-stage, three-stage, and four-stage robust models. The three robust models are also compared to two currently practiced deterministic methods, one that does not account for radioresistance and one that assumes a constant radioresistance. All five models are evaluated on a clinical prostate case. The three robust models improve control of the tumor compared to the deterministic model ignoring radioresistance, at comparable radiation dose to critical organs. The robust models also reduce tumor overdose and organ dose compared to the deterministic model assuming a constant radioresistance. Increasing the number of intermediate imaging leads to further improvements, especially on tumor dose criteria under best-case and nominal scenarios. Under the worst-case, intermediate images provide no additional benefit as robust optimization inherently protects against the worst-case. The proposed method is generic and can include additional sources of uncertainties that reduce the effect of radiation.