Computational Methods for Breast Cancer Molecular Profiling through Routine Histopathology: A Review
This work is a review that synthesizes existing research on AI-driven techniques for biomarker detection in breast cancer, aiming to advance personalized cancer care by bridging the gap between AI and clinical application.
This review addresses the problem of enabling molecular profiling for breast cancer from routine histopathology images using AI, highlighting that these technologies can extract various biomarkers from H&E-stained images to support treatment decisions without costly assays.
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.