Work

Linear energy transfer optimization mitigates biological model discrepancies in carbon ion radiotherapy for pelvic osteosarcoma

Year: 2026

Type: article

Abstract: Background and Purpose. In the combined treatment of osteosarcoma with a carbon ion radiotherapy (CIRT) boost, large pelvic tumors are associated with significantly lower tumor control compared to craniofacial localization. This could be linked to lower dose-averaged linear energy transfer (LET d ) values in the target and larger uncertainties related to the model used for relative biological effectiveness (RBE) calculation. This study aimed to bridge the difference between RBE-weighted doses from two clinically used models, the Local Effect Model I (LEM-I) and the modified microdosimetric kinetic model (mMKM), using LET d optimization. Materials and methods Fifteen pelvic osteosarcoma CIRT plans, initially optimized with LEM-I, were re-optimized to achieve higher target LET d while preserving target and organ at risk (OAR) dose objectives. Both dose- and LET-optimized plans were recalculated with mMKM, and dose metrics were compared across the groups. Robustness against setup and range uncertainties and changes in patient anatomy (using control CTs) was investigated. Results LET d optimization resulted in target LET d escalation without compromising LEM-I target coverage. The LET-optimized group also demonstrated better mMKM dose coverage and a significant decrease (up to 9% of prescription) in the disparity between LEM-I and mMKM target dose. OAR doses remained largely unaffected by model choice or optimization strategy. The robustness loss with LET d optimization was minor (intra-patient dose variability increased by 0.3–2.3%) compared to the reduction in model-to-model variation. Conclusions When adequately balanced with physical uncertainties, LET d optimization could become a useful strategy for bridging RBE model predictions and increasing the efficacy of CIRT.

Source: Physics and Imaging in Radiation Oncology

DOI: 10.1016/j.phro.2026.101113