Exploring Genetic-histologic Relationships in Breast Cancer
This addresses the problem of expensive and limited gene sequencing for breast cancer diagnosis and treatment planning, though it is an incremental improvement on existing methods.
This work uses deep learning to predict six genomic biomarkers from breast cancer histopathology images, achieving AUROC improvements of 0.02 to 0.13 over existing methods while also providing morphological insights.
The advent of digital pathology presents opportunities for computer vision for fast, accurate, and objective solutions for histopathological images and aid in knowledge discovery. This work uses deep learning to predict genomic biomarkers - TP53 mutation, PIK3CA mutation, ER status, PR status, HER2 status, and intrinsic subtypes, from breast cancer histopathology images. Furthermore, we attempt to understand the underlying morphology as to how these genomic biomarkers manifest in images. Since gene sequencing is expensive, not always available, or even feasible, predicting these biomarkers from images would help in diagnosis, prognosis, and effective treatment planning. We outperform the existing works with a minimum improvement of 0.02 and a maximum of 0.13 AUROC scores across all tasks. We also gain insights that can serve as hypotheses for further experimentations, including the presence of lymphocytes and karyorrhexis. Moreover, our fully automated workflow can be extended to other tasks across other cancer subtypes.