Diagnostic value and external validation of biparametric magnetic resonance imaging radiomics in clinically significant prostate cancer.

ABSTRACT:

Background

Given the significant economic burden of prostate cancer (PCa), its diagnostic methods need to be improved. The limitations of the subjective Prostate Imaging Reporting and Data System (PI-RADS) underscore the need for generalizable radiomics in clinically significant PCa (csPCa). This study aimed to build a machine-learning model based on biparametric magnetic resonance imaging (bpMRI) to diagnose csPCa.

Methods

Prognostic model development: This study retrospectively included the data of 445 patients from two centers, of whom 206 had csPCa and 239 had clinically non-significant PCa (ncsPCa). The training set comprised 120 csPCa patients and 141 ncsPCa patients. The test set comprised 52 csPCa patients and 61 ncsPCa patients. The external validation comprised 34

SUBMITTER: Xing H 

PROVIDER: S-EPMC12433168 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature