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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Vydáno v:J Radiat Res
Hlavní autoři: Kai, Yudai, Arimura, Hidetaka, Ninomiya, Kenta, Saito, Tetsuo, Shimohigashi, Yoshinobu, Kuraoka, Akiko, Maruyama, Masato, Toya, Ryo, Oya, Natsuo
Médium: Artigo
Jazyk:Inglês
Vydáno: Oxford University Press 2020
Témata:
Regular Paper
On-line přístup: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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