Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy

The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam...

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Detalhes bibliográficos
Publicado no:J Radiat Res
Main Authors: Kai, Yudai, Arimura, Hidetaka, Ninomiya, Kenta, Saito, Tetsuo, Shimohigashi, Yoshinobu, Kuraoka, Akiko, Maruyama, Masato, Toya, Ryo, Oya, Natsuo
Formato: Artigo
Idioma:Inglês
Publicado em: Oxford University Press 2020
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Regular Paper
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7246080/
https://ncbi.nlm.nih.gov/pubmed/31994702
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/jrr/rrz105
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