Department of Pathology, Radboudumc, Nijmegen, the Netherlands.
Department of Pathology, Canisius-Wilhelmina Hospital, Nijmegen, the Netherlands.
Mod Pathol. 2024 Feb;37(2):100417. doi: 10.1016/j.modpat.2023.100417. Epub 2023 Dec 27.
Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.
子宫内膜活检对于因异常子宫出血或遗传性子宫内膜癌风险而就诊的女性的诊断具有重要意义。一般来说，约 10%的子宫内膜活检显示出需要特定治疗的子宫内膜（前）恶性肿瘤。由于对大多数良性病例的诊断评估会给病理学家带来大量的工作负担，因此人工智能（AI）辅助活检预选可以优化工作流程。本研究旨在评估基于日常实践全切片图像而不是高度选择的图像进行子宫内膜活检（子宫内膜 Pipelle 活检计算机辅助诊断）的 AI 辅助诊断的可行性。子宫内膜活检分为 6 种临床相关类别，定义如下：非代表性、正常、非肿瘤性、非典型增生、非典型增生和恶性。在 91 例子宫内膜活检中，15 名病理学家对这些分类中的每一种进行了分类，并评估了他们的分类一致性。接下来，一个算法（基于总共 2819 例子宫内膜活检进行训练）对相同的 91 例病例进行了评分，我们使用病理学家的分类作为参考标准来比较其性能。病理学家之间的组内一致性为中度，平均 Cohen's kappa 为 0.51，而对于良性与（前）恶性的二分类，一致性为高度，平均 Cohen's kappa 为 0.66。AI 算法在 6 个类别中的表现稍差，Cohen's kappa 为 0.43，但对于二分类，Cohen's kappa 为 0.65，与病理学家的表现相当。AI 辅助诊断子宫内膜活检在区分良性和（前）恶性子宫内膜组织方面是可行的，即使是在未经选择的病例上进行训练。子宫内膜前病变仍然是病理学家和 AI 算法的挑战。为了实现更精细的子宫内膜活检 AI 辅助诊断解决方案，涵盖前病变和恶性诊断，需要进一步提高诊断的可靠性。
