Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.

ABSTRACT:

Objective

This study aims to evaluate the diagnostic value of machine learning-based MRI imaging in differentiating benign and malignant prostate cancer and detecting clinically significant prostate cancer (csPCa, defined as Gleason score ≥7) using systematic review and meta-analysis methods.

Methods

Electronic databases (PubMed, Web of Science, Cochrane Library, and Embase) were systematically searched for predictive studies using machine learning-based MRI imaging for prostate cancer diagnosis. Sensitivity, specificity, and area under the curve (AUC) were used to assess the diagnostic accuracy of machine learning-based MRI imaging for both benign/malignant prostate cancer and csPCa.

Results

A total of 12 studies met the inclusion criteria, with 3474 patients include

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12909110 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature