Indrani Bhattacharya, Yash S Khandwala, Sulaiman Vesal, Wei Shao, Qianye Yang, Simon J C Soerensen, Richard E Fan, Pejman Ghanouni, Christian A Kunder, James D Brooks, Yipeng Hu, Mirabela Rusu, Geoffrey A Sonn
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引用次数: 0
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.
期刊介绍:
Therapeutic Advances in Urology delivers the highest quality peer-reviewed articles, reviews, and scholarly comment on pioneering efforts and innovative studies across all areas of urology.
The journal has a strong clinical and pharmacological focus and is aimed at clinicians and researchers in urology, providing a forum in print and online for publishing the highest quality articles in this area. The editors welcome articles of current interest across all areas of urology, including treatment of urological disorders, with a focus on emerging pharmacological therapies.