Login / Signup

Update in the molecular classification of endometrial carcinoma.

Alicia Léon-Castillo
Published in: International journal of gynecological cancer : official journal of the International Gynecological Cancer Society (2023)
The pathological classification of endometrial carcinomas, one of the cornerstones in patient clinical management, has traditionally been based on morphologic features. However, this classification system does not fully reflect the biological diversity of endometrial carcinomas and has limited reproducibility. In the last decade, several studies have reported the strong prognostic value of the molecular endometrial carcinoma subgroups and, more recently, its potential to inform adjuvant treatment decisions. This has in turn resulted in a transition from a purely morphological classification towards an integrated histological and molecular system in the latest World Health Organization (WHO) classification of tumors of female reproductive organs. The new European treatment guidelines combine the molecular subgroups with traditional clinicopathological features in order to guide treatment decision-making. Accurate molecular subgroup assignment is therefore essential for adequate patient management. This review aims to address caveats and evolution of molecular techniques relevant in the implementation of the molecular endometrial carcinoma classification, as well as challenges in the integration of the molecular subgroups with traditional clinicopathological features.
Keyphrases
  • machine learning
  • deep learning
  • healthcare
  • decision making
  • high grade
  • case report
  • high resolution