A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model

A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model

PurposeThis study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).MethodsWe retrospectively analyzed 232 participants from our institution...

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Bibliographic Details
Main Authors: Xiang Liu, Zhong-Xin Zhang, Bing Zheng, Min Xu, Xin-Yu Cao, Hai-Ming Huang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Oncology
Subjects:
bi-parametric
ultrasound
deep learning
radiomics
prostate cancer
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1538854/full
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