Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data...

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Detalles Bibliográficos
Autores principales: Zhao, Litao, Bao, Jie, Qiao, Xiaomeng, Jin, Pengfei, Ji, Yanting, Li, Zhenkai, Zhang, Ji, Su, Yueting, Ji, Libiao, Shen, Junkang, Zhang, Yueyue, Niu, Lei, Xie, Wanfang, Hu, Chunhong, Shen, Hailin, Wang, Ximing, Liu, Jiangang, Tian, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852176/
https://www.ncbi.nlm.nih.gov/pubmed/36409317
http://dx.doi.org/10.1007/s00259-022-06036-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852176/
https://www.ncbi.nlm.nih.gov/pubmed/36409317
http://dx.doi.org/10.1007/s00259-022-06036-9

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