Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters
Deep learning-based histologic analysis effectively stratifies HGSC into clinically relevant prognostic groups, highlighting the role of mitochondrial dynamics and energy metabolism in disease progression. This method offers a novel approach to HGSC categorization.
Authors
- Byungsoo Ahn (ORCID: https://orcid.org/0009-0007-3641-9590)
- Eunhyang Park (ORCID: https://orcid.org/0000-0003-2658-5054)
Institutions
- Yonsei University (KR)
- Severance Hospital (KR)
Publication Details
- Journal
- Journal of Pathology and Translational Medicine
- Published
- 2025-02-18
- DOI
- https://doi.org/10.4132/jptm.2024.10.23
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96