Brachytherapy delivers highly conformal doses for malignancies ranging from pancreatic to head-and-neck cancers, yet today’s treatment-planning systems still depend on extensive manual manipulation and dose engines of uncertain accuracy. We introduce BrachyPlan, an end-to-end pre-operative framework that automates low-dose-rate (LDR) brachytherapy planning while preserving clinical precision. The system combines: 1) a real-time Monte Carlo dose engine that produces high-fidelity, heterogeneity-aware dose maps, and 2) a dose-guided inverse-planning algorithm that optimally places seeds to meet target coverage and organ-at-risk constraints. In retrospective experiments, BrachyPlan reproduced ground-truth Monte Carlo dose-volume histograms (DVHs) with < 5 % error across all metrics, achieved V100 > 95 % for every clinical target, and cut planning time to one-eighth of the manual workflow, with minimal manual intervention and optimal efficiency. To our knowledge, this is the first platform that unites a clinically validated dose calculation algorithm with real-time inverse optimization, delivering both sub-5 % dosimetric accuracy and unprecedented efficiency for LDR brachytherapy.

BrachyPlan: A fine-grained efficient dose-guided inverse planning strategy for low-dose-rate brachytherapy

Liu, Jiaxuan;Corino, Valentina;
2026-01-01

Abstract

Brachytherapy delivers highly conformal doses for malignancies ranging from pancreatic to head-and-neck cancers, yet today’s treatment-planning systems still depend on extensive manual manipulation and dose engines of uncertain accuracy. We introduce BrachyPlan, an end-to-end pre-operative framework that automates low-dose-rate (LDR) brachytherapy planning while preserving clinical precision. The system combines: 1) a real-time Monte Carlo dose engine that produces high-fidelity, heterogeneity-aware dose maps, and 2) a dose-guided inverse-planning algorithm that optimally places seeds to meet target coverage and organ-at-risk constraints. In retrospective experiments, BrachyPlan reproduced ground-truth Monte Carlo dose-volume histograms (DVHs) with < 5 % error across all metrics, achieved V100 > 95 % for every clinical target, and cut planning time to one-eighth of the manual workflow, with minimal manual intervention and optimal efficiency. To our knowledge, this is the first platform that unites a clinically validated dose calculation algorithm with real-time inverse optimization, delivering both sub-5 % dosimetric accuracy and unprecedented efficiency for LDR brachytherapy.
2026
Interventional oncology
Inverse optimization
LDR brachytherapy
Medical image computing
Monte Carlo dose calculation
Treatment planning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1310033
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