PLoS medicine
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Multicenter Study
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. ⋯ In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.