Fremond Sarah, Koelzer Viktor Hendrik, Horeweg Nanda, Bosse Tjalling
Department of Pathology, Leiden University Medical Center (LUMC), Leiden, Netherlands.
Department of Pathology and Molecular Pathology, University Hospital and University of Zürich, Zürich, Switzerland.
Front Oncol. 2022 Aug 18;12:928977. doi: 10.3389/fonc.2022.928977. eCollection 2022.
Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into mutated (mut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.
子宫内膜癌（EC）的诊断正在演变为一个分子层面愈发重要的系统。传统的基于组织学亚型的分类已转向基于分子的分类，该分类将EC分为突变型（mut）、错配修复缺陷型（MMRd）和p53异常型（p53abn），其余的EC则为无特定分子特征型（NSMP）。分子EC分类已被纳入世界卫生组织2020年分类和2021年欧洲治疗指南，因为它为患者管理提供了更好的基础。因此，将分子分类与组织病理学变量相结合已成为近期EC研究的关键重点。病理学家已经观察并描述了与特定基因组改变相关的几种形态学特征，但这些特征似乎不足以根据分子亚组准确地对患者进行分类。这就要求病理学家在常规检查中依靠分子辅助检测。在这个新时代，在个体患者基础上为组织学和分子特征赋予临床相关权重变得越来越具有挑战性。深度学习（DL）技术为多模态图像和分子数据集与临床结果的综合分析开辟了新的选择。在其他癌症中的概念验证研究表明，从苏木精和伊红（H&E）染色的肿瘤玻片图像预测分子改变具有可观的准确性。这表明在EC中也可以识别出一些与分子改变相关的形态学特征，从而扩展了目前对分子驱动的EC分类的理解。在这篇综述中，我们报告了目前文献中确定的分子EC分类的形态学特征。鉴于EC诊断中的新挑战，因此，本综述讨论了DL可能发挥的潜在支持作用，通过展望所有使用DL对各种癌症类型（重点是EC）的组织病理学图像进行的相关研究。最后，我们探讨了DL可能如何塑造未来EC患者的管理。
