Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.

Abstract

Objectives: This study aims to evaluate the overall impact of incorporating deep learning (DL) with magnetic resonance imaging (MRI) for improving diagnostic performance in the detection and stratification of prostate cancer (PC).

Methods: A systematic search was conducted in the PubMed database to identify relevant studies. The QUADAS-2 tool was employed to assess the scientific quality, risk of bias, and applicability of primary diagnostic accuracy studies. Additionally, adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines was evaluated to determine the extent of heterogeneity among the included studies. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.

Results: A total of 29 articles involving 17,954 participants were included in the study. The median agreement to the 42 CLAIM checklist items across studies was 61.90% (IQR: 57.14-66.67, range: 40.48-80.95). Most studies utilized T2-weighted imaging (T2WI) and/or apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) as input for evaluating the performance of DL-based architectures. Notably, the detection and stratification of PC in the transition zone was the least explored area.

Conclusions: DL demonstrates significant advancements in the rapid, sensitive, specific, and robust detection and stratification of PC. Promising applications include enhancing the quality of DWI, developing advanced DL models, and designing innovative nomograms or diagnostic tools to improve clinical decision-making.