Artificial intelligence in prostate cancer imaging: A mini-review of current applications and future directions.

Authors

Abstract

This mini-review synthesizes evidence from recent studies to provide an updated perspective on current applications, methodological challenges, and future directions for artificial intelligence (AI) in multiparametric magnetic resonance imaging (mpMRI) based prostate cancer (CaP) imaging. CaP remains the most frequently diagnosed noncutaneous malignancy among men worldwide and a leading cause of cancer-related mortality. mpMRI has become the reference imaging modality for detection, localization, and risk stratification, with the Prostate Imaging-Reporting and Data System (PI-RADS) improving standardization. However, inter-reader variability and the time-intensive nature of mpMRI interpretation persist, even among expert radiologists. AI, encompassing machine learning (ML) and deep learning (DL) methods, offers the potential to enhance CaP imaging by improving accuracy, consistency, and efficiency. Applications include automated lesion detection and segmentation, PI-RADS scoring standardization, and radiomics-based risk prediction. Radiomics enables the extraction of high-dimensional quantitative features from mpMRI, which, when integrated with clinical or genomic data, can improve predictive modeling for clinically significant CaP, extracapsular extension, and lymph node metastasis. Despite rapid advancements, challenges remain in data heterogeneity, generalizability, lack of standardized feature extraction, and limited external validation. The "black-box" nature of many DL models also complicates clinical trust and regulatory approval. Future directions include the integration of explainable AI, federated learning for privacy-preserving multi-institutional training, and real-time AI assistance during targeted biopsies or active surveillance.

Tags

Topics