Morpho-Genomic Deep Learning for Ovarian Cancer Subtype and Gene Mutation Prediction from Histopathology

TL;DR

This study presents a deep learning approach combining morphometric and image features to classify ovarian cancer subtypes and predict gene mutations directly from histopathology images, demonstrating high accuracy and potential for precision diagnostics.

Contribution

Introduces a hybrid deep learning pipeline integrating nuclear morphometry and image features for ovarian cancer subtype and gene mutation prediction from histopathology images, a novel approach in this domain.

Findings

Achieved 84.2% overall subtype classification accuracy.

Demonstrated gene mutation inference with AUCs above 0.73 for key genes.

Identified nuclear solidity and eccentricity as key predictors for TP53 mutation.

Abstract

Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to late diagnosis and extensive heterogeneity across subtypes. Current diagnostic methods are limited in their ability to reveal underlying genomic variations essential for precision oncology. This study introduces a novel hybrid deep learning pipeline that integrates quantitative nuclear morphometry with deep convolutional image features to perform ovarian cancer subtype classification and gene mutation inference directly from Hematoxylin and Eosin (H&E) histopathological images. Using $\sim45,000$ image patches sourced from The Cancer Genome Atlas (TCGA) and public datasets, a fusion model combining a ResNet-50 Convolutional Neural Network (CNN) encoder and a Vision Transformer (ViT) was developed. This model successfully captured both local morphological texture and global tissue context. The pipeline achieved a robust overall subtype classification accuracy of $84.2\%$ (Macro AUC of $0.87 \pm 0.03$). Crucially, the model demonstrated the capacity for gene mutation inference with moderate-to-high accuracy: $AUC_{TP53} = 0.82 \pm 0.02$, $AUC_{BRCA1} = 0.76 \pm 0.04$, and $AUC_{ARID1A} = 0.73 \pm 0.05$. Feature importance analysis established direct quantitative links, revealing that nuclear solidity and eccentricity were the dominant predictors for TP53 mutation. These findings validate that quantifiable histological phenotypes encode measurable genomic signals, paving the way for cost-effective, precision histopathology in ovarian cancer triage and diagnosis.