Fig. 4: MOMA predicts overall survival outcomes of stage III colorectal cancer patients using digital histopathology images, with validation in multiple independent cohorts.

A MOMA successfully distinguishes the shorter-term survivors from longer-term survivors using histopathology images (two-sided log-rank test P-value=0.02). Results from the TCGA held-out test set are shown. B The machine learning model derived from MOMA is successfully validated in an independent external validation set from the Nurses’ Health Study and Health Professionals Follow-up Study cohorts (two-sided log-rank test P-value<0.05). C We further validate our overall survival prediction model in PLCO, a nationwide multi-center study cohort (two-sided log-rank test P-value = 0.04). D Model prediction of a patient with longer-term overall survival. The model focuses on regions of cancerous tissue and cancer-associated stroma when making the prediction in this example. E Interpretation of the overall survival prediction model. The prediction of a patient with shorter-term survival is shown in this figure panel. Cancerous tissue, cancer-associated stroma, and smooth muscle receive high attention weights in the overall survival prediction task. TUM: colorectal adenocarcinoma epithelium; STR: cancer-associated stroma; MUC: mucus; MUS: smooth muscle; LYM: lymphocytes.