Poster Poster Program Therapy Physics

Using Rapidplan to Guide and Benchmark Monaco Treatment Planning for Malignant Pleural Mesothelioma

Abstract
Purpose

Treatment planning for malignant pleural mesothelioma (MPM) is challenging due to target geometry and organ-at-risk sparing requirements. This study uses RapidPlan-predicted dose volume histograms (DVHs) to guide Monaco planning for TrueBeam and Versa delivery comparing dosimetric results across plans.

Methods

Twenty MPM patients were retrospectively selected. RapidPlan VMAT plans (2–4 arcs with contralateral lung avoidance) were generated in Eclipse v18.1 and re-planned in Monaco for TrueBeam and Versa delivery using constrained optimization, producing 60 plans. All plans used 6 MV beams with a prescription of 25 Gy in 5 fractions. Monaco optimization constraints were created using a physician-guided automation template. Dosimetric comparisons were performed using the Wilcoxon signed-rank test to evaluate plan quality, organ-at-risk sparing, target coverage, and delivery consistency across both treatment platforms clinically relevant.

Results

RapidPlans in Eclipse as well as plans in Monaco all met the dose constraints by generous margins of as much as 42% or 12.7 Gy depending on the dose constraint, while requiring 1-3 iterations from either a starting DVH estimation or template. For the same target coverage, total lung V12 Gy was lower (p<0.001) for Monaco Versa plans at 32% (aim < 37%) while slightly higher at 34.5% for delivery on the Truebeam with Monaco or Eclipse RapidPlan. Total and contralateral lung V3 Gy were comparable (aim < 80%), while heart mean and V18 Gy were within 1 Gy and 1% of each other on average. Similar trends were observed for liver, stomach, esophagus mean, ipsilateral kidney V10 Gy and bowel maximum dose.

Conclusion

RapidPlan can help guide in quality assurance/benchmarking of mesothelioma plans when utilizing a new planning system to plan the same case with both planning systems able to achieve the goals. Planning with constraint optimization helped prioritize and reduce low dose to the lung with other dosimetric tradeoffs.

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