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| Home > Publications database > Linear energy transfer optimization mitigates biological model discrepancies in carbon ion radiotherapy for pelvic osteosarcoma |
| Home > Publications database > Linear energy transfer optimization mitigates biological model discrepancies in carbon ion radiotherapy for pelvic osteosarcoma |
| Journal Article | DKFZ-2026-02742 |
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2026
Elsevier Science
Amsterdam [u. a.]
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 craniofaciallocalization. This could be linked to lower dose-averaged linear energy transfer (LETd) values in the target andlarger uncertainties related to the model used for relative biological effectiveness (RBE) calculation. This studyaimed to bridge the difference between RBE-weighted doses from two clinically used models, the local effectmodel I (LEM-I) and the modified microdosimetric kinetic model (mMKM), using LETd optimization.Materials and methods: Fifteen pelvic osteosarcoma CIRT plans, initially optimized with LEM-I, were re-optimizedto achieve higher target LETd while preserving target and organ at risk (OAR) dose objectives. Both dose- andLET-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) wasinvestigated.Results: LETd optimization resulted in target LETd escalation without compromising LEM-I target coverage. TheLET-optimized group also demonstrated better mMKM dose coverage and a significant decrease (up to 9% ofprescription) in the disparity between LEM-I and mMKM target dose. OAR doses remained largely unaffected bymodel choice or optimization strategy. The robustness loss with LETd optimization was minor (intra-patient dosevariability increased by 0.3–2.3%) compared to the reduction in model-to-model variation.Conclusions: When adequately balanced with physical uncertainties, LETd optimization could become a usefulstrategy for bridging RBE model predictions and increasing the efficacy of CIRT.
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