Twilt Jasper J, van Leeuwen Kicky G, Huisman Henkjan J, Fütterer Jurgen J, de Rooij Maarten
Department of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
Diagnostics (Basel). 2021 May 26;11(6):959. doi: 10.3390/diagnostics11060959.
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
由于磁共振成像（MRI）在前列腺癌（PCa）诊断中具有前期作用，人们提出了多种人工智能（AI）应用来辅助PCa的诊断和检测。在本综述中，我们概述了当前该领域的情况，包括2018年至2021年2月期间的研究，描述了用于（1）病变分类和（2）PCa病变检测的AI算法。我们对纳入的59项研究的评估表明，大多数研究是针对PCa病变分类任务进行的（66%），其次是PCa病变检测（34%）。研究表明，队列规模差异很大，患者人数在18至499人之间（中位数 = 162），同时性能验证方法也各不相同。此外，85%的研究报告了独立诊断准确性，而15%的研究展示了AI对诊断思维效能的影响，这表明PCa AI应用临床效用的证据有限。为了将AI引入PCa评估的临床工作流程，需要利用外部验证和临床工作流程实验进一步验证AI应用的稳健性和通用性。
