Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.
Institute for Artificial Intelligence, the State Key Laboratory of Intelligence Technology and Systems, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China.
Lab Invest. 2022 Jun;102(6):641-649. doi: 10.1038/s41374-022-00742-6. Epub 2022 Feb 17.
Gastric cancer possesses great histological and molecular diversity, which creates obstacles for rapid and efficient diagnoses. Classic diagnoses either depend on the pathologist's judgment, which relies heavily on subjective experience, or time-consuming molecular assays for subtype diagnosis. Here, we present a deep learning (DL) system to achieve interpretable tumor differentiation grade and microsatellite instability (MSI) recognition in gastric cancer directly using hematoxylin-eosin (HE) staining whole-slide images (WSIs). WSIs from 467 patients were divided into three cohorts: the training cohort with 348 annotated WSIs, the testing cohort with 88 annotated WSIs, and the integration testing cohort with 31 original WSIs without tumor contour annotation. First, the DL models comprehensibly achieved tumor differentiation recognition with an F1 values of 0.8615 and 0.8977 for poorly differentiated adenocarcinoma (PDA) and well-differentiated adenocarcinoma (WDA) classes. Its ability to extract pathological features about the glandular structure formation, which is the key to distinguishing between PDA and WDA, increased the interpretability of the DL models. Second, the DL models achieved MSI status recognition with a patient-level accuracy of 86.36% directly from HE-stained WSIs in the testing cohort. Finally, the integrated end-to-end system achieved patient-level MSI recognition from original HE staining WSIs with an accuracy of 83.87% in the integration testing cohort with no tumor contour annotation. The proposed system, therefore, demonstrated high accuracy and interpretability, which can potentially promote the implementation of artificial intelligence healthcare.
胃癌具有很大的组织学和分子多样性，这给快速、有效的诊断带来了障碍。经典的诊断方法要么依赖于病理学家的判断，而这种判断很大程度上依赖于主观经验，要么依赖于耗时的分子检测来进行亚型诊断。在这里，我们提出了一种深度学习（DL）系统，该系统可以直接使用苏木精-伊红（HE）染色全切片图像（WSIs），实现对胃癌的可解释肿瘤分化等级和微卫星不稳定性（MSI）的识别。来自 467 名患者的 WSIs 分为三个队列：包含 348 张注释 WSIs 的训练队列、包含 88 张注释 WSIs 的测试队列和包含 31 张原始 WSIs 的整合测试队列，这些原始 WSIs 没有肿瘤轮廓注释。首先，DL 模型全面实现了肿瘤分化识别，低分化腺癌（PDA）和高分化腺癌（WDA）类别的 F1 值分别为 0.8615 和 0.8977。它能够提取关于腺体结构形成的病理特征，这是区分 PDA 和 WDA 的关键，从而提高了 DL 模型的可解释性。其次，DL 模型直接从测试队列中的 HE 染色 WSIs 中实现了 MSI 状态识别，患者级别的准确率为 86.36%。最后，该端到端集成系统在没有肿瘤轮廓注释的整合测试队列中，从原始 HE 染色 WSIs 中实现了患者级别的 MSI 识别，准确率为 83.87%。因此，所提出的系统表现出了较高的准确性和可解释性，这可能有助于推动人工智能医疗的实施。
