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...
Saved in:
| Main Authors: | , |
|---|---|
| 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: | |
| 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!
|
| _version_ | 1849762327453237248 |
|---|---|
| author | Byungsoo Ahn Eunhyang Park |
| author_facet | Byungsoo Ahn Eunhyang Park |
| author_sort | Byungsoo Ahn |
| collection | DOAJ |
| description | 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. Methods We applied the Histomic Atlases of Variation Of Cancers model to whole slide images from The Cancer Genome Atlas dataset for ovarian cancer. Histologically distinct tumor clones were grouped into common histomic clusters. Principal component analysis and K-means clustering classified HGSC samples into three groups: highly differentiated (HD), intermediately differentiated (ID), and lowly differentiated (LD). Results HD tumors showed diverse patterns, lower densities, and stronger eosin staining. ID tumors had intermediate densities and balanced staining, while LD tumors were dense, patternless, and strongly hematoxylin-stained. RNA sequencing revealed distinct patterns in mitochondrial oxidative phosphorylation and energy metabolism, with upregulation in the HD, downregulation in the LD, and the ID positioned in between. Survival analysis showed significantly lower overall survival for the LD compared to the HD and ID, underscoring the critical role of mitochondrial dynamics and energy metabolism in HGSC progression. Conclusions 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. |
| format | Article |
| id | doaj-art-96feba3862ab47d98311c9cd39adee2f |
| institution | DOAJ |
| issn | 2383-7837 2383-7845 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Korean Society of Pathologists & the Korean Society for Cytopathology |
| record_format | Article |
| series | Journal of Pathology and Translational Medicine |
| spelling | doaj-art-96feba3862ab47d98311c9cd39adee2f2025-08-20T03:05:45ZengKorean Society of Pathologists & the Korean Society for CytopathologyJournal of Pathology and Translational Medicine2383-78372383-78452025-03-015929110410.4132/jptm.2024.10.2317139Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clustersByungsoo Ahn0Eunhyang Park1 Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, KoreaBackground 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. Methods We applied the Histomic Atlases of Variation Of Cancers model to whole slide images from The Cancer Genome Atlas dataset for ovarian cancer. Histologically distinct tumor clones were grouped into common histomic clusters. Principal component analysis and K-means clustering classified HGSC samples into three groups: highly differentiated (HD), intermediately differentiated (ID), and lowly differentiated (LD). Results HD tumors showed diverse patterns, lower densities, and stronger eosin staining. ID tumors had intermediate densities and balanced staining, while LD tumors were dense, patternless, and strongly hematoxylin-stained. RNA sequencing revealed distinct patterns in mitochondrial oxidative phosphorylation and energy metabolism, with upregulation in the HD, downregulation in the LD, and the ID positioned in between. Survival analysis showed significantly lower overall survival for the LD compared to the HD and ID, underscoring the critical role of mitochondrial dynamics and energy metabolism in HGSC progression. Conclusions 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.http://www.jpatholtm.org/upload/pdf/jptm-2024-10-23.pdfcarcinoma, ovarian epithelialoxidative phosphorylationenergy metabolismdeep learning |
| spellingShingle | Byungsoo Ahn Eunhyang Park Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters Journal of Pathology and Translational Medicine carcinoma, ovarian epithelial oxidative phosphorylation energy metabolism deep learning |
| title | Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters |
| title_full | Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters |
| title_fullStr | Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters |
| title_full_unstemmed | Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters |
| title_short | Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning–based histomic clusters |
| title_sort | categorizing high grade serous ovarian carcinoma into clinically relevant subgroups using deep learning based histomic clusters |
| topic | carcinoma, ovarian epithelial oxidative phosphorylation energy metabolism deep learning |
| url | http://www.jpatholtm.org/upload/pdf/jptm-2024-10-23.pdf |
| work_keys_str_mv | AT byungsooahn categorizinghighgradeserousovariancarcinomaintoclinicallyrelevantsubgroupsusingdeeplearningbasedhistomicclusters AT eunhyangpark categorizinghighgradeserousovariancarcinomaintoclinicallyrelevantsubgroupsusingdeeplearningbasedhistomicclusters |