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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Ngā kaituhi matua:	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
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Elsevier 2026-05-01
Rangatū:	Physics and Imaging in Radiation Oncology
Ngā marau:	Proton radiotherapy Linear energy transfer Deep learning Pencil beam scanning Double scattering Uncertainty quantification
Urunga tuihono:	http://www.sciencedirect.com/science/article/pii/S2405631626000989
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!