Deep learning for dose-averaged linear energy transfer estimation in pencil-beam scanning and double scattering proton radiotherapy plans with uncertainty-aware external validation

Background and Purpose:: Accounting for the linear energy transfer (LET) in proton radiotherapy may reduce treatment-related side effects. When Monte Carlo (MC) simulations are unavailable, deep-learning (DL) surrogate models can be applied. We develop DL LET models for brain tumour patients and ass...

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Autors principals:	Aaron Kieslich, Yerik Singh, Martina Palkowitsch, Sebastian Starke, Fabian Hennings, Esther G.C. Troost, Mechthild Krause, Jona Bensberg, Armin Lühr, Feline Heinzelmann, Christian Bäumer, Beate Timmermann, Nicolas Depauw, Helen A. Shih, Steffen Löck
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2026-05-01
Col·lecció:	Physics and Imaging in Radiation Oncology
Matèries:	Proton radiotherapy Linear energy transfer Deep learning Pencil beam scanning Double scattering Uncertainty quantification
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S2405631626000989
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