Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.

doi:10.3390/ijms27167341

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Community

Back to search

Article

Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.

International journal of molecular sciences - 17 Aug 2026

Jeong Yesul, Hong Sungman, Ahn Sangjeong, Lee Sung Hak

Abstract excerpt

Endometrial cancer is one of the most rapidly increasing gynaecological malignancies worldwide. The clinically adapted molecular classification of endometrial carcinoma, derived from The Cancer Genome Atlas, comprises four major subtypes: POLE-mutated, mismatch repair-deficient, p53-abnormal expression, and no specific molecular profile. Its clinical implementation has improved prognostic stratification, risk...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artificial Intelligence
Share to Biomarkers, Tumor
Share to Endometrial Neoplasms
Share to POLE gene

Source

Open in PubMed Open full text

Identifiers

PMID: 42653345
DOI: 10.3390/ijms27167341
PMCID: PMC13512974

Genes

POLE

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation