3D-AttenNet model can predict clinically significant prostate cancer in PI-RADS category 3 patients: a retrospective multicenter study

3D-AttenNet model can predict clinically significant prostate cancer in PI-RADS category 3 patients: a retrospective multicenter study

Abstract Purposes The presence of clinically significant prostate cancer (csPCa) is equivocal for patients with prostate imaging reporting and data system (PI-RADS) category 3. We aim to develop deep learning models for re-stratify risks in PI-RADS category 3 patients. Methods This retrospective stu...

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Bibliographic Details
Main Authors: Jie Bao, Litao Zhao, Xiaomeng Qiao, Zhenkai Li, Yanting Ji, Yueting Su, Libiao Ji, Junkang Shen, Jiangang Liu, Jie Tian, Ximing Wang, Hailin Shen, Chunhong Hu
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:Insights into Imaging
Subjects:
Deep learning
MRI
Clinically significant prostate cancer
PI-RADS
Online Access:https://doi.org/10.1186/s13244-024-01896-1
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