Computational Pathology Detection of Hypoxia-Induced Morphological Changes in Breast Cancer.

Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of AI in computational histopathology to evaluate hypoxia in breast cancer. Weakly Supervised Deep Learning (WSDL) models can accurately detect morphological changes associated with hypoxia in routine Hematoxylin and Eosin (H&E) whole slide images (WSI). Our model, HypOxNet, was trained on H&E WSI from breast cancer primary sites (n=1016) at 40x magnification using data from The Cancer Genome Atlas (TCGA). We utilized the Hypoxia Buffa signature to measure hypoxia scores, which ranged from -43 to +47, and stratified the samples into hypoxic and normoxic based on these scores. This stratification represented the weak labels associated with each WSI. HypOxNet achieved an average Area Under the Curve (AUC) of 0.82 on test sets, identifying significant differences in cell morphology between hypoxic and normoxic tissue regions. Importantly, once trained, the HypOxNet model requires only the readily available H&E slides, making it especially valuable in low-resource settings where additional gene expression assays are not available. These AI-based hypoxia detection models can potentially be extended to other tumor types and seamlessly integrated into pathology workflows, offering a fast, cost-effective alternative to molecular testing.