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
Department of Radiology, Stanford University School of Medicine, 1201 Welch Road, Stanford, CA 94305, USA.
Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Ther Adv Urol. 2022 Oct 10;14:17562872221128791. doi: 10.1177/17562872221128791. eCollection 2022 Jan-Dec.
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.
众多研究探讨了人工智能（AI）在为放射科医生、病理科医生和泌尿科医生提供前列腺癌检测、风险分层及管理方面的诊断支持中的作用。本综述全面概述了有关人工智能模型在以下方面应用的相关文献：（1）在放射影像（磁共振成像和超声成像）上检测前列腺癌；（2）在前列腺活检组织的组织病理学图像上检测前列腺癌；（3）协助支持前列腺癌检测的任务（前列腺腺体分割、磁共振成像 - 组织病理学配准、磁共振成像 - 超声配准）。我们既讨论了这些人工智能模型协助前列腺癌诊断临床工作流程的潜力，也讨论了当前的局限性，包括训练数据集、算法和评估标准的变异性。我们还讨论了当前面临的挑战以及弥合前列腺癌人工智能学术研究与改善常规临床护理的商业解决方案之间差距所需的条件。
