Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review

TL;DR

This review discusses AI-driven computational methods that analyze routine histopathology images to identify molecular biomarkers in breast cancer, aiming to enhance personalized treatment without costly molecular tests.

Contribution

It provides a comprehensive overview of recent AI techniques for molecular biomarker detection from histopathology images and discusses key challenges for clinical translation.

Findings

AI enables extraction of genomic, transcriptomic, proteomic, and metabolomic biomarkers from H&E images

Recent methods show promise in non-invasive, cost-effective molecular profiling

Major challenges include data variability and algorithm robustness

Abstract

Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for diagnostic purposes; however, they are now recognized for their potential in molecular profiling, which provides deeper insights into cancer prognosis and treatment response. Recent advancements in artificial intelligence (AI) have enabled digital pathology to analyze histopathologic images for both targeted molecular and broader omic biomarkers, marking a pivotal step in personalized cancer care. These technologies offer the capability to extract various biomarkers such as genomic, transcriptomic, proteomic, and metabolomic markers directly from the routine hematoxylin and eosin (H&E) stained images, which can support treatment decisions without the need for costly molecular assays. In this work, we provide a comprehensive review of AI-driven techniques for biomarker detection, with a focus on diverse omic biomarkers that allow novel biomarker discovery. Additionally, we analyze the major challenges faced in this field for robust algorithm development. These challenges highlight areas where further research is essential to bridge the gap between AI research and clinical application.