Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer

Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer

ObjectiveThis study aimed to develop MRI-based radiomics machine learning models for predicting adverse pathological prognostic features in prostate cancer and to explore the feasibility of integrating radiomics with clinical characteristics to improve preoperative risk stratification, addressing th...

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Bibliographic Details
Main Authors: Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Oncology
Subjects:
prostate cancer
magnetic resonance imaging
radiomics
machine learning
biparametric MRI
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2025.1625158/full
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https://www.frontiersin.org/articles/10.3389/fonc.2025.1625158/full

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