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

Abstract

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<OS<5years, N=45) patients (5year survival p-value=0.5, 5year relative risk=1.32±0.09). However, we find that novel integrative models combining whole slide images, clinical variables, and mutation signatures can improve patient stratification for moderate risk patients (5year survival p-value=6.69e-30, 5year relative risk=5.32±0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset generated by our team (3year survival p-value=1.14e-09, 5year survival p-value=2.15e-05, 3year relative risk=3.25±0.06, 5year relative-risk=3.07±0.08). The integrative model substantially outperforms models using only images or only clinical variables, indicating beneficial cross-talk between the data types. Pathologist review of image-based heatmaps suggests that nuclear shape, nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high risk, while low risk regions tend to have more regular and small cells. The improved stratification of colorectal cancer patients from our computational methods can be beneficial for preemptive development of management and treatment plans for individual patients, as well as for informed enrollment of patients in clinical trials.