AI tool predicts colon cancer survival, treatment response harvardmed NatureComms
To demonstrate the extensibility of our MOMA platform to different prediction tasks, we connected our machine learning framework with the Weibull modeling methodsto predict overall survival and progression-free survival outcomes of early-stage and stage III colorectal cancer patients.
We applied the same image tiling, pre-processing, and color normalization methods to preprocess histopathology images from these external cohorts. We reported the AUROC , concordance index , and log-rank testWe further identified human-interpretable pathology features employed by our machine learning models to obtain biological insights into the connections between histopathology morphology and molecular profiles.
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