Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study

Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study

Abstract Objective To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa). Materials and methods A total of 878 PCa patients from 4 hospitals were retrospectively collected, al...

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Bibliographic Details
Main Authors: Kexin Wang, Ning Luo, Zhaonan Sun, Xiangpeng Zhao, Lilan She, Zhangli Xing, Yuntian Chen, Chunlei He, Pengsheng Wu, Xiangpeng Wang, ZiXuan Kong
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:Insights into Imaging
Subjects:
Gleason score
Prostate cancer
Multiparametric magnetic resonance imaging
Deep learning
Radiomics
Online Access:https://doi.org/10.1186/s13244-024-01865-8
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https://doi.org/10.1186/s13244-024-01865-8

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