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Classification of Epithelial Tissues: Overview01:22

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Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
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Categorizing high-grade serous ovarian carcinoma into clinically relevant subgroups using deep learning-based

Byungsoo Ahn1, Eunhyang Park1

  • 1Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

Journal of Pathology and Translational Medicine
|February 18, 2025
PubMed
Summary

Histologic analysis of high-grade serous ovarian carcinoma (HGSC) effectively stratifies patients into prognostic groups. This approach reveals the critical role of mitochondrial energy metabolism in HGSC progression and offers new categorization methods.

Keywords:
Carcinoma, ovarian epithelialDeep learningEnergy metabolismOxidative phosphorylation

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Area of Science:

  • Computational pathology
  • Cancer genomics
  • Ovarian cancer research

Background:

  • High-grade serous ovarian carcinoma (HGSC) presents significant heterogeneity, complicating clinical classification.
  • Understanding histomorphological variations in HGSC is crucial for prognostic stratification and personalized medicine.

Purpose of the Study:

  • To apply a deep learning model to stratify HGSC based on histomorphological features.
  • To investigate the relationship between distinct histologic subtypes and molecular/metabolic profiles.
  • To correlate histologic subtypes with patient survival outcomes.

Main Methods:

  • Whole slide images from The Cancer Genome Atlas (TCGA) ovarian cancer dataset were analyzed using the Histomic Atlases of Variation Of Cancers (HAVOC) model.
  • Principal component analysis and K-means clustering categorized HGSC samples into highly differentiated (HD), intermediately differentiated (ID), and lowly differentiated (LD) groups.
  • RNA sequencing and survival analysis were performed on the classified sample groups.

Main Results:

  • Distinct histomorphological patterns, densities, and staining characteristics were observed for HD, ID, and LD groups.
  • RNA sequencing revealed differential patterns of mitochondrial oxidative phosphorylation and energy metabolism across the three groups.
  • Lowly differentiated (LD) HGSC tumors showed significantly poorer overall survival compared to highly differentiated (HD) and intermediately differentiated (ID) tumors.

Conclusions:

  • Deep learning-based histologic analysis provides effective stratification of HGSC into prognostically relevant groups.
  • Mitochondrial dynamics and energy metabolism are key factors in HGSC progression, as highlighted by this stratification.
  • This study introduces a novel computational approach for HGSC categorization, improving prognostic accuracy.