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Hypoxia occurs when tumour cells outgrow their blood supply, leading toregions of low oxygen levels within the tumour. Calculating hypoxia levels canbe an important step in understanding the biology of tumours, their clinicalprogression and response to treatment. This study demonstrates a novelapplication of deep learning to evaluate hypoxia in the context of breastcancer histomorphology. More precisely, we show that Weakly Supervised DeepLearning (WSDL) models can accurately detect hypoxia associated features inroutine Hematoxylin and Eosin (H&E) whole slide images (WSI). We trained andevaluated a deep Multiple Instance Learning model on tiles from WSI H&E tissuefrom breast cancer primary sites (n=240) obtaining on average an AUC of 0.87 ona left-out test set. We also showed significant differences between features ofhypoxic and normoxic tissue regions as distinguished by the WSDL models. SuchDL hypoxia H&E WSI detection models could potentially be extended to othertumour types and easily integrated into the pathology workflow withoutrequiring additional costly assays.

Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images