Department of Pathology, Amsterdam University Medical Centers, Amsterdam, The Netherlands; Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands; Quantitative Healthcare Analysis Group, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Machine Learning Lab, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.
Department of Pathology, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
Mod Pathol. 2024 Aug;37(8):100531. doi: 10.1016/j.modpat.2024.100531. Epub 2024 Jun 1.
Histopathological assessment of esophageal biopsies is a key part in the management of patients with Barrett esophagus (BE) but prone to observer variability and reliable diagnostic methods are needed. Artificial intelligence (AI) is emerging as a powerful tool for aided diagnosis but often relies on abstract test and validation sets while real-world behavior is unknown. In this study, we developed a 2-stage AI system for histopathological assessment of BE-related dysplasia using deep learning to enhance the efficiency and accuracy of the pathology workflow. The AI system was developed and trained on 290 whole-slide images (WSIs) that were annotated at glandular and tissue levels. The system was designed to identify individual glands, grade dysplasia, and assign a WSI-level diagnosis. The proposed method was evaluated by comparing the performance of our AI system with that of a large international and heterogeneous group of 55 gastrointestinal pathologists assessing 55 digitized biopsies spanning the complete spectrum of BE-related dysplasia. The AI system correctly graded 76.4% of the WSIs, surpassing the performance of 53 out of the 55 participating pathologists. Furthermore, the receiver-operating characteristic analysis showed that the system's ability to predict the absence (nondysplastic BE) versus the presence of any dysplasia was with an area under the curve of 0.94 and a sensitivity of 0.92 at a specificity of 0.94. These findings demonstrate that this AI system has the potential to assist pathologists in assessment of BE-related dysplasia. The system's outputs could provide a reliable and consistent secondary diagnosis in challenging cases or be used for triaging low-risk nondysplastic biopsies, thereby reducing the workload of pathologists and increasing throughput.
食管活检的组织病理学评估是 Barrett 食管 (BE) 患者管理的关键部分，但容易受到观察者变异的影响，需要可靠的诊断方法。人工智能 (AI) 作为辅助诊断的有力工具正在兴起，但它通常依赖于抽象的测试和验证集，而实际行为是未知的。在这项研究中，我们开发了一个基于深度学习的 2 阶段 AI 系统，用于评估 BE 相关异型增生的组织病理学，以提高病理工作流程的效率和准确性。该 AI 系统在 290 张全切片图像 (WSI) 上进行了开发和训练，这些图像在腺体和组织水平上进行了注释。该系统旨在识别单个腺体、分级异型增生并分配 WSI 级别的诊断。通过比较我们的 AI 系统与 55 位国际和异质的胃肠病理学家对 55 个数字化活检的评估性能，评估了所提出的方法。该 AI 系统正确分级了 76.4%的 WSI，超过了 55 位参与病理学家中的 53 位。此外，接受者操作特征分析表明，该系统预测有无任何异型增生的能力的曲线下面积为 0.94，灵敏度为 0.92，特异性为 0.94。这些发现表明，该 AI 系统有可能协助病理学家评估 BE 相关异型增生。该系统的输出可以在具有挑战性的情况下提供可靠和一致的二级诊断，或者用于对低风险无异型增生的活检进行分类，从而减少病理学家的工作量并提高吞吐量。
