The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning - Fremond, Sarah; Koelzer, Viktor Hendrik; Horeweg, Nanda; Bosse, Tjalling (Frontiers in Oncology, 2022)
Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histologic subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair-deficient (MMRd), p53 abnormal (p53abn) and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the WHO-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 histologic 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 tumour 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 DL could have, this 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.