Diagnostic Performance of Artificial Intelligence Based on Biparametric MRI for Clinically Significant Prostate Cancer: A Systematic Review and Meta-analysis.

Objectives: This meta-analysis aimed to systematically evaluate the diagnostic performance of artificial intelligence (AI) applied to biparametric magnetic resonance imaging (bpMRI) for identifying clinically significant prostate cancer (csPCa).

Methods: A comprehensive systematic review was conducted following PRISMA-DTA guidelines, searching PubMed, Embase, and Web of Science databases. Studies focus on AI algorithms based on bpMRI in diagnosis csPCa were included. Bivariate random-effects models synthesized sensitivity, specificity, and area under the curve (AUC). Heterogeneity was assessed using I² statistics, with subgroup analyses exploring variations across AI methodologies, AI models, study designs, and geographical regions.

Results: Nineteen studies were included, encompassing 4594 patients in internal validation sets, 795 in external validation sets, and 897 in radiologist cohorts. AI models based on bpMRI exhibited notable diagnostic performance, with internal validation revealing an average sensitivity of 0.88 (95% CI: 0.84-0.92), average specificity of 0.79 (95% CI: 0.73-0.84), and an average AUC of 0.91 (95% CI: 0.88-0.93). External validation confirmed these results with a average sensitivity of 0.85 (95% CI: 0.78-0.90), average specificity of 0.83 (95% CI: 0.69-0.91), and an average AUC of 0.91 (95% CI: 0.88-0.93). In contrast, radiologist assessments showed lower performance with an average AUC of 0.78 (95% CI: 0.74-0.81).

Conclusion: AI applied to bpMRI demonstrates excellent diagnostic performance for csPCa, representing a promising noninvasive approach that may potentially outperform traditional radiological interpretations. However, notable heterogeneity across studies and limited sample size for radiologists and external validation sets suggests the need for caution. To substantiate these findings and investigate clinical applicability, additional prospective studies are essential.