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

Abstract

Understanding the tumor hypoxic microenvironment is crucial for grasping tumor biology, clinical progression, and treatment responses. This study presents a novel application of artificial intelligence in computational histopathology to evaluate hypoxia in breast cancer. Weakly supervised deep learning models can accurately detect morphologic changes associated with hypoxia in routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs). Our model, HypOxNet, was trained on H&E-stained WSIs from breast cancer primary sites (n = 1016) at ×40 magnification using data from The Cancer Genome Atlas. We used 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 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-stained slides, making it especially valuable in low-resource settings where additional gene expression assays are not available. These artificial intelligence-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.