前列腺活检的病理学检查由于每个案例的幻灯片数量庞大而耗时。本回顾性研究中，我们验证了一个基于深度学习的前列腺癌（PCA）检测和格里森分级（AI工具），应用于活检样本。从高容量病理学研究所分析了五个外部患者群的多灶性前列腺活检。总计对5922个被数字化的H&E切片进行了评估，代表了423个病例中的7473个活检核心（使用三个扫描仪进行数字化）。两个带肿瘤数据集（活检核心数分别为227和159）由包括有经验的泌尿外科病理学家（n = 11）在国际小组中进行了格里森分级验证。依据不同的测试群体，对于肿瘤活检的检测，灵敏度、特异度和阴性预测值（NPV）在0.971-1.000、0.875-0.976和0.988-1.000的范围内。在一些活检幻灯片中，AI工具能够正确检测到一开始被病理学家忽略的肿瘤组织。大多数误报为可疑的癌症或癌症模仿者。对于单个病理学家的格里森分级一致性的平方加权kappa等级在0.62-0.80之间（AI工具为0.77），对于两个分级数据集，平方加权kappa等级在0.64-0.76之间（AI工具为0.72）。在病理学家达成分级一致意见的情况下，AI工具的kappa等级分别为0.903和0.855。在外部验证中，PCA检测分类器对于活检病例的PCA检测显示出了高准确性，不受所用研究所和扫描仪的影响。格里森分级的高一致性水平在经验丰富的泌尿外科病理学家和AI工具之间无法区分。© 2023. Nature Publishing Group UK.

Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971-1.000, 0.875-0.976, and 0.988-1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62-0.80 (0.77 for AI tool) and 0.64-0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool.© 2023. Nature Publishing Group UK.