Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality

Colorectal cancers are the fourth most commonly diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. We first observe that deep learning models based only on whole slide images (WSIs) from The Cancer Genome Atlas accurately separate high risk (OS<3years, N=38) from low risk (OS>5years, N=25) patients (AUC=0.81±0.08, 5year survival p-value=2.13e-25, 5year relative risk=5.09±0.05) though such models are less effective at predicting OS for moderate risk (3years更多