ArticleInternational journal of particle therapy2025
Comprehensive Robustness Evaluation of Proton and Carbon-Ion Plans in Thoracic Cancer Treatment.
Article in International journal of particle therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Comparative evaluation of the Mayo Clinic Florida microdosimetric kinetic model and mMKM for carbon ion treatment planning: A matRad-based analysis.Journal of applied clinical medical physics · 2026Article
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Authors and funding
10 authors.
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Abstract
Purpose: To evaluate the impact of setup errors, range uncertainty, and respiratory motion on dose distributions for clinically delivered pencil beam scanning proton and carbon-ion plans. Materials and Methods: A retrospective analysis was conducted on 23 lung cancer and trachea adenoid cystic carcinoma patients who received treatment at our center. Plans were generated using Syngo with planning target volume-based optimization. Dose reconstruction was performed using TReatment planning for Particles 4D. The plans robustness evaluation was performed using two methods: a worst scenarios conventional evaluation (WSCE) with 21 scenarios and a worst scenarios statistical evaluation (WSSE) with 100 randomly sampled scenarios. On top of the 3D evaluation considering setup error and range error, a 4D evaluation was performed considering motion-induced error. Results: The overall target dose ΔD95% was -2.37% ± 1.55% (mean ± standard deviation [SD]) of the prescribed dose (PD) and -2.62% ± 2.08% for 3DWSSE and 4DWSSE, respectively. The WSCE method often underestimated the dose by approximately 5% for ΔD95%. The induced uncertainties had limited impact on mean doses for Lungs-iGTV and heart. However, a ΔD1cc greater than 5% of PD was observed for the esophagus and trachea. Conclusion: Conventional robustness evaluation showed significantly reduced target coverage, yet it considers highly improbable worst-case scenarios. Comprehensive WSSE enables the identification of critical patients without compromising plan quality by avoiding overestimation and compensating for unrealistic error scenarios.
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