A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

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作者： Sebastian,Starke ; Aaron,Kieslich ; Martina,Palkowitsch ; Fabian,Hennings ; Esther,G C Troost ; Mechthild,Krause ; Jona,Bensberg ; Christian,Hahn ; Feline,Heinzelmann ; Christian,Bäumer ; Armin,Lühr ; Beate,Timmermann ; Steffen,Löck

关键词 NTCP models brain tumor deep learning linear energy transfer proton-beam therapy relative biological effectiveness

主题词蒙特卡罗法(Monte Carlo Method);人类(Humans);脑肿瘤(Brain Neoplasms);传能线密度(Linear Energy Transfer);放射治疗计划, 计算机辅助(Radiotherapy Planning, Computer-Assisted);放射治疗剂量(Radiotherapy Dosage)

DOI 10.1088/1361-6560/ad64b7

PMID 39019053

发布时间 2024-10-04

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Physics in medicine and biology

2024年69卷16期

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A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

Physics in medicine and biology

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Physics in medicine and biology

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A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

Physics in medicine and biology

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Physics in medicine and biology

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