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

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

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 r...

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Bibliographic Details
Main Authors: Sarah Fremond, Viktor Hendrik Koelzer, Nanda Horeweg, Tjalling Bosse
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Oncology
Subjects:
endometrial carcinoma
tumour morphology
computer vision
deep learning
molecular classification
phenotype
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.928977/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2022.928977/full

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