Artificial Intelligence–Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis

Background: Artificial intelligence (AI) techniques, particularly those employing machine learning (ML) and deep learning (DL) to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE). Objective: This meta-analysis explores the diagnostic performance of artificial intelligence-enabled imaging techniques versus radiologists for predicting preoperative EPE in prostate cancer (PCa). Methods: We conducted a systematic review of literature from PubMed, Embase, and Web of Science, following PRISMA-DTA guidelines. The included studies applied AI techniques to predict EPE using mpMRI and PSMA PET imaging. Sensitivity, specificity, and area under the curve (AUC) for both internal and external validation sets were extracted and combined using a bi-variate random-effects model. The quality of the included studies was assessed using the modified QUADAS-2 tool. Results: A total of 21 studies were analyzed. The mpMRI-based AI demonstrated a pooled sensitivity of 0.78, specificity of 0.76, and an AUC of 0.84, significantly outperforming traditional radiologists, whose pooled sensitivity for detecting EPE was 0.69, specificity was 0.72, and AUC was 0.76. Conversely, the PSMA PET-based AI showed a pooled sensitivity of 0.73, specificity of 0.61, and an AUC of 0.74, indicating moderate performance but no significant advantage over either mpMRI-based AI or radiologists. Conclusions: This study indicates that the mpMRI-based AI model has higher sensitivity and AUC values compared to radiologists. However, the PSMA PET-based AI shows no additional advantage in diagnostic performance for predicting preoperative EPE in prostate cancer, whether compared to mpMRI or human-interpreted PET. Limitations include the retrospective design and high heterogeneity which may introduce bias and affect generalizability. Larger, diverse cohorts are essential for confirming these findings and optimizing the integration of AI in clinical practice.