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.

The value of multimodal functional magnetic resonance imaging in differentiating p53abn from p53wt endometrial carcinoma

“…p53abn-type EC has a wider histological spectrum, which includes a high-grade serous carcinoma and clear cell carcinoma (type II cancer), etc. (33), and tends to be more active in mitosis (15). The proportion of type IIin p53abn-type EC was significantly higher than that of the p53wt type ( P < 0.001), which was consistent with this view.…”

Computational Image and Molecular Analysis Reveal Unique Prognostic Features of Immune Architecture in African Versus European American Women with Endometrial Cancer

“…Unfortunately, several extant risk prediction models have been shown to not be equitable across different populations. A notable example is Oncotype DX, a prognostic and predictive gene-expression assay that was recently shown to not be prognostic in black women 50 . This underscores the urgency of refining and diversifying precision medicine assays to ensure inclusivity across diverse patient populations.…”

Pré-diagnóstico de câncer de mama usando imagens histopatológicas com pré-processamento morfológico por meio de algoritmos clássicos e redes neurais profundas

“…Como trabalhos futuros, propomos investigar o efeito de outros operadores morfológicos (e.g. , fechamento por reconstruc ¸ão), além de explorar de forma mais aprofundada outros trabalhos que combinam técnicas de Deep Learning com Morfologia Matemática [3], [4], [8].…”

“…O diagnóstico baseado em imagens histopatológicas é considerado o "padrão-ouro" para vários tipos de câncer, incluindo o câncer de mama [2]. Contudo, o análise manual destas imagens requer grande esforc ¸o e experiência por parte do médico especialista, além de demandar um tempo considerável para conclusão da análise [3], [4]. Para atenuar essa problemática, têm surgido diversas contribuic ¸ões no campo de diagnóstico auxiliado por computador, em particular, via análise digital de imagens histopatológicas para a classificac ¸ão de tumores em benignos ou malignos, bem como para a obtenc ¸ão dos tipos histológicos associados a eles.…”