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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Detalles Bibliográficos
Autores principales: Kai, Yudai, Arimura, Hidetaka, Ninomiya, Kenta, Saito, Tetsuo, Shimohigashi, Yoshinobu, Kuraoka, Akiko, Maruyama, Masato, Toya, Ryo, Oya, Natsuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Regular Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246080/
https://www.ncbi.nlm.nih.gov/pubmed/31994702
http://dx.doi.org/10.1093/jrr/rrz105

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7246080/
https://www.ncbi.nlm.nih.gov/pubmed/31994702
http://dx.doi.org/10.1093/jrr/rrz105

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