  Hypoxia occurs when tumour cells outgrow their blood supply, leading to
regions of low oxygen levels within the tumour. Calculating hypoxia levels can
be an important step in understanding the biology of tumours, their clinical
progression and response to treatment. This study demonstrates a novel
application of deep learning to evaluate hypoxia in the context of breast
cancer histomorphology. More precisely, we show that Weakly Supervised Deep
Learning (WSDL) models can accurately detect hypoxia associated features in
routine Hematoxylin and Eosin (H&E) whole slide images (WSI). We trained and
evaluated a deep Multiple Instance Learning model on tiles from WSI H&E tissue
from breast cancer primary sites (n=240) obtaining on average an AUC of 0.87 on
a left-out test set. We also showed significant differences between features of
hypoxic and normoxic tissue regions as distinguished by the WSDL models. Such
DL hypoxia H&E WSI detection models could potentially be extended to other
tumour types and easily integrated into the pathology workflow without
requiring additional costly assays.
