Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters

Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters

Background High-grade serous ovarian carcinoma (HGSC) exhibits significant heterogeneity, posing challenges for effective clinical categorization. Understanding the histomorphological diversity within HGSC could lead to improved prognostic stratification and personalized treatment approaches. Method...

Full description

Saved in:
Bibliographic Details
Main Authors: Byungsoo Ahn, Eunhyang Park
Format: Article
Language:English
Published: Korean Society of Pathologists & the Korean Society for Cytopathology 2025-03-01
Series:Journal of Pathology and Translational Medicine
Subjects:
carcinoma, ovarian epithelial
oxidative phosphorylation
energy metabolism
deep learning
Online Access:http://www.jpatholtm.org/upload/pdf/jptm-2024-10-23.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.jpatholtm.org/upload/pdf/jptm-2024-10-23.pdf

Similar Items