Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study

PurposePatients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI)...

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Bibliographic Details
Main Authors: Chuan Zhou, Yun-Feng Zhang, Sheng Guo, Yu-Qian Huang, Xiao-Ni Qiao, Rong Wang, Lian-Ping Zhao, De-Hui Chang, Li-Ming Zhao, Ming-Xu Da, Feng-Hai Zhou
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-03-01
Series:Frontiers in Oncology
Subjects:
radiomics
pathomics
castration-resistant prostate cancer
deep learning
multi-modal
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2024.1287995/full

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https://www.frontiersin.org/articles/10.3389/fonc.2024.1287995/full

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