旨在评估对主动监测 (AS) 前列腺癌患者进行连续活检的癌症检测人工智能 (AI) 算法。使用预定义的方法对前列腺癌研究国际主动监测 (PRIAS) 队列中的总共 180 名患者进行前瞻性监测标准。通过基于人工智能的内部癌症检测算法对 2011 年至 2020 年的诊断和重新活检切片 (n = 4744) 进行扫描和分析。分析该算法的敏感性、特异性和准确性，以预测是否需要积极治疗。评估诊断时癌症大小、前列腺特异性抗原 (PSA) 水平和 PSA 密度的预后特性。AI 算法正确检测癌症区域的灵敏度和特异性分别为 0.96 和 0.73。以原始病理报告诊断为参考方法。病理学家估计的癌症面积与人工智能检测到的癌症大小高度相关（r = 0.83）。通过使用 AI 算法，63% 的载玻片不需要病理学家阅读，因为它们被归类为良性，但有可能丢失 0.55% 的含有癌症的载玻片。诊断时的活检癌症含量和 PSA 密度被发现可以预测患者是否继续接受 AS 治疗或停止积极治疗。基于 AI 的活检癌症检测算法可用于减少病理学家在 AS 队列中的工作量。检测到的癌症数量与病理学家测量的癌症长度密切相关，并且该算法在发现甚至很小的癌症区域方面也表现良好。据我们所知，这是第一份关于数字病理学中基于人工智能的算法的报告，该算法用于检测 AS 患者队列中的癌症。© 2024 作者。 BJU International 约翰·威利 (John Wiley) 出版

To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS).A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate-specific antigen (PSA) level and PSA density at diagnosis were evaluated.The sensitivity and specificity of the AI algorithm was 0.96 and 0.73, respectively, for correct detection of cancer areas. Original pathology report diagnosis was used as the reference method. The area of cancer estimated by the pathologists correlated highly with the AI detected cancer size (r = 0.83). By using the AI algorithm, 63% of the slides would not need to be read by a pathologist as they were classed as benign, at the risk of missing 0.55% slides containing cancer. Biopsy cancer content and PSA density at diagnosis were found to be prognostic of whether the patient stayed on AS or was discontinued for active treatment.The AI-based biopsy cancer detection algorithm could be used to reduce the pathologists' workload in an AS cohort. The detected cancer amount correlated well with the cancer length measured by the pathologist and the algorithm performed well in finding even small areas of cancer. To our knowledge, this is the first report on an AI-based algorithm in digital pathology used to detect cancer in a cohort of patients on AS.© 2024 The Author(s). BJU International published by John Wiley & Sons Ltd on behalf of BJU International.