Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
A model-based risk-minimizing proton treatment planning concept for brain injury prevention in low-grade glioma patients.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Sallem H, Harrabi S, Traneus E, Herfarth K, Debus J, Bauer J
Аннотация
PURPOSE: Late-occurring contrast-enhancing brain lesions (CEBLs) have been observed on MRI follow-up in low-grade glioma (LGG) patients post-proton therapy. Predictive risk-models for this endpoint identified a dose-averaged linear energy transfer (LETd)-dependent proton relative biological effectiveness (RBE) effect on CEBL occurrence and increased radiosensitivity of the cerebral periventricular region (VP4mm). This work aimed to design a stable risk-minimizing treatment planning (TP) concept addressing these intertwined risk factors through a classically formulated optimization problem.
MATERIAL AND METHODS: The concept was developed in RayStation-research 11B IonPG featuring a variable-RBE-based optimizer involving 20 LGG patients with varying target volume localizations and risk-factor contributions. Classical cost functions penalizing dose, dose-volume-histogram points, and equivalent uniform dose were used to formulate the optimization problem, and a new set of structures was introduced to actively spare the VP4mm, control high LETd regions, and de-escalate the dose outside the gross tumor volume. Target volume coverage and organ-at-risk sparing were robustly evaluated, and Normal Tissue Complication Probabilities (NTCP) for CEBL occurrence were quantified.
RESULTS: The concept yielded stable optimization outcomes for all considered subjects. Risk hot spots were successfully mitigated, and an NTCP reduction of up to 79 % was observed compared to conventional TP while maintaining target coverage, demonstrating the feasibility of the chosen model-based approach.
CONCLUSION: With the proposed TP protocol, we close the gap between predictive risk-modeling and practical risk-mitigation in the clinic and provide a concept for CEBL avoidance with the potential to advance treatment precision for LGG patients.
MATERIAL AND METHODS: The concept was developed in RayStation-research 11B IonPG featuring a variable-RBE-based optimizer involving 20 LGG patients with varying target volume localizations and risk-factor contributions. Classical cost functions penalizing dose, dose-volume-histogram points, and equivalent uniform dose were used to formulate the optimization problem, and a new set of structures was introduced to actively spare the VP4mm, control high LETd regions, and de-escalate the dose outside the gross tumor volume. Target volume coverage and organ-at-risk sparing were robustly evaluated, and Normal Tissue Complication Probabilities (NTCP) for CEBL occurrence were quantified.
RESULTS: The concept yielded stable optimization outcomes for all considered subjects. Risk hot spots were successfully mitigated, and an NTCP reduction of up to 79 % was observed compared to conventional TP while maintaining target coverage, demonstrating the feasibility of the chosen model-based approach.
CONCLUSION: With the proposed TP protocol, we close the gap between predictive risk-modeling and practical risk-mitigation in the clinic and provide a concept for CEBL avoidance with the potential to advance treatment precision for LGG patients.