The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Abstract

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 DNA polymerase epsilon mutated (POLEmut), 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.

Keywords: computer vision; deep learning; endometrial carcinoma; histopathology image; molecular classification; phenotype; tumour morphology; whole slide image.

Figure 1

The diagnostic algorithm of the molecular classification of endometrial cancer, associated prognosis, diagnostic test, and potential adjuvant treatment regime. EC, endometrial cancer; NGS, panel next-generation sequencing; POLEmut, polymerase epsilon mutated; MMRd, mismatch repair deficient; NSMP, no specific molecular profile; p53abn, p53 abnormal; DDR, DNA damage response; PD-L1, programmed death ligand.

Figure 2

The evolving role of morphology in endometrial cancer diagnostics. EC, endometrial cancer; POLEmut, polymerase epsilon mutated; MMRd, mismatch repair deficient; NSMP, no specific molecular profile; p53abn, p53 abnormal.

Figure 3

A selection of prototypical morphological features found in POLE-mutated endometrial cancer (POLEmut EC): (A) at least 50% solid growth; (B) hyperchromatic tumor giant cells; (C) a dense peri-tumoral and intra-epithelial infiltrate of lymphocytes; and (D) tertiary lymphoid structures (TLS).

Figure 4

A selection of prototypical morphological features found in mismatch repair deficient endometrial cancer (MMRd EC): (A) solid growth; (B) glandular architecture; (C) a dense to moderate peri-tumoral and intra-epithelial infiltrate of lymphocytes; and (D) tertiary lymphoid structures (TLS).

Figure 5

A selection of prototypical morphological features found in p53 abnormal endometrial cancer (p53abn EC): (A) (micro-)papillary glandular architecture; (B) glands with ragged luminal surface; (C) brisk mitotic activity; and (D) strong nuclear atypia.

Figure 6

A selection of prototypical morphological features found in non-specific molecular profile endometrial cancer (NSMP EC): (A) glands with smooth luminal borders; (B) squamous differentiation; (C) microcystic elongated and fragmented (MELF) type of invasion; and (D) mild nuclear atypia.