Background

Robust optimization (RO) has been proposed to mitigate breathing motion uncertainty during treatment in intensity-modulated radiation therapy (IMRT) planning for breast or lung cancer. RO is a pessimistic approach that implicitly trades off average-case for worst-case treatment plan quality. Pareto robust optimization (PRO) provides a mechanism for improving nonworst-case plan outcomes, but often remains overly conservative in the average case.

Purpose

The goal of this study is to characterize the trade-off between the optimality of robust IMRT plans in the worst case and the treatment quality in nonworst-case realizations of breathing motion. We provide a light Pareto robust optimization (LPRO) method for IMRT and test its clinical viability for improving the average-case plan quality while preserving robustness, in comparison to RO and PRO plans.

Methods

Five clinical left-sided breast cancer patients were included in the study, each with an associated 4D-CT dataset approximating their breathing cycle. Using simulation, 50 different breathing patterns were generated for each patient. A first-stage optimization was solved with the objective of cardiac sparing while ensuring robustness on the target dose under breathing uncertainty. Next, a second-stage objective of overdose minimization was considered to improve plan quality in a controlled LPRO framework. For the simulated breathing scenarios, the trade-off between loss of average cardiac sparing at worst-case and the overdose to the breast was quantified by calculating the accumulated dose for each plan in each breathing scenario. Finally, the RO, PRO, and LPRO plans were each evaluated using eight clinical dose–volume criteria on the target and organs at risk.

Results

The LPRO models allowed for significantly sharper dose falloffs in the expected dose instances, relative to both RO and PRO models. Plans began looking valid for delivery with average allowances of as little as +0.1 Gy additional dose to the heart, and most patients experienced diminishing returns beyond +0.2 Gy.

Conclusions

Without sacrificing robustness, the LPRO approach produces viable plans with true total-target irradiation. Furthermore, the plans produced were able to reduce the nonworst-case downside typical of RO, without the characteristic overdosing or average-case pessimism seen in prior models.