A review of artificial intelligence in prostate cancer detection on imaging. (October 2022)

• Record Type: Journal Article
• Authors: Bhattacharya, Indrani; Khandwala, Yash S.; Vesal, Sulaiman; Shao, Wei; Yang, Qianye; Soerensen, Simon J.C.; Fan, Richard E.; Ghanouni, Pejman; Kunder, Christian A.; Brooks, James D.; Hu, Yipeng; Rusu, Mirabela; Sonn, Geoffrey A.
• Is Part Of: Therapeutic advances in urology. Volume 14(2022)
• Publication Date: 2022-10
• Subjects: artificial intelligence -- histopathology images -- magnetic resonance imaging -- prostate cancer diagnosis -- registration -- ultrasound images; Urology -- Periodicals; Urologic Diseases -- therapy -- Periodicals; Genital Diseases, Male -- therapy -- Periodicals; Urologie -- Périodiques; 616.6005
• DOI: 10.1177/17562872221128791
• Languages: English
• ISSNs: 1756-2872

Abstract

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.