肝癌腺癌的诊断在常规病理学中经常发生，并对临床决策产生关键影响。然而，准确诊断可能具有挑战性，并且通常需要整合临床、放射和免疫组织化学信息。我们提出了一个深度学习模型（HEPNET），使用苏木精和伊红染色的全层切片图像，以临床级准确度区分原发性和继发性肝腺癌的最常见形式的肝内胆管细胞癌（iCCA）和结肠直肠癌肝转移（CRM）。 HEPNET在海德堡大学医院的一个包含571个接受手术切除或活检的患者中，以一种分层方式随机选取的456名患者的714,589个图像小块上进行了训练。模型性能在包含115名患者的内部测试集上进行了评估，并在由美因茨大学医院招募的159名患者上进行了外部验证。 在内部测试集上，HEPNET在接受者操作特征曲线下的面积（AUROC）为0.994（95% CI 0.989-1.000），在患者层面上的准确度为96.522％（95％CI 94.521-98.694％）。在外部测试集上验证后，得出了一个AUROC为0.997（95% CI 0.995-1.000）的结果，相应的准确度为98.113％（95％CI 96.907-100.000％）。HEPNET在一项对50名患者的读者研究中超过了六位病理学专家的表现（P = .0005），提高了住院医师的表现水平，减少了潜在的下游分析。 我们提供了一个具备临床级性能的即用工具，可以通过提供最终诊断和指导辅助测试来促进常规病理学。将HEPNET整合到病理实验室中可能优化诊断工作流程，同时节省与测试相关的劳动力和成本。 版权所有 ©2023 AGA学会。由Elsevier Inc.出版。保留所有权利。

Diagnosis of adenocarcinoma in the liver is a frequent scenario in routine pathology and has a critical impact on clinical decision-making. However, rendering a correct diagnosis can be challenging and often requires the integration of clinical, radiological, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma (iCCA) from colorectal liver metastasis (CRM) as the most frequent primary and secondary forms of liver adenocarcinoma with clinical-grade accuracy using hematoxylin and eosin-stained whole-slide images.HEPNET was trained on 714 589 image tiles from 456 patients who were randomly selected in a stratified manner from a pool of 571 patients who underwent surgical resection or biopsy at Heidelberg University Hospital. Model performance was evaluated on a hold-out internal test set comprising 115 patients and externally validated on 159 patients recruited at Mainz University Hospital.On the hold-out internal test set, HEPNET achieved an area under the receiver operating characteristic curve (AUROC) of 0.994 (95% CI 0.989-1.000) and an accuracy of 96.522% (95% CI 94.521-98.694%) at the patient level. Validation on the external test set yielded an AUROC of 0.997 (95% CI 0.995-1.000), corresponding to an accuracy of 98.113% (95% CI 96.907-100.000%). HEPNET surpassed the performance of six pathology experts with different levels of experience in a reader study of 50 patients (P=.0005), boosted the performance of resident pathologists to the level of senior pathologists, and reduced potential downstream analyses.Here, we provide a ready-to-use tool with a clinical-grade performance that may facilitate routine pathology by rendering a definitive diagnosis and guiding ancillary testing. The incorporation of HEPNET into pathology laboratories may optimize the diagnostic workflow, complemented by test-related labor and cost savings.Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.