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There exists unexplained diverse variation within the predefined colon cancerstages using only features either from genomics or histopathological wholeslide images as prognostic factors. Unraveling this variation will bring aboutimproved in staging and treatment outcome, hence motivated by the advancementof Deep Neural Network libraries and different structures and factors withinsome genomic dataset, we aggregate atypical patterns in histopathologicalimages with diverse carcinogenic expression from mRNA, miRNA and DNAMethylation as an integrative input source into an ensemble deep neural networkfor colon cancer stages classification and samples stratification into low orhigh risk survival groups. The results of our Ensemble Deep ConvolutionalNeural Network model show an improved performance in stages classification onthe integrated dataset. The fused input features return Area under curveReceiver Operating Characteristic curve (AUC ROC) of 0.95 compared with AUC ROCof 0.71 and 0.68 obtained when only genomics and images features are used forthe stage's classification, respectively. Also, the extracted features wereused to split the patients into low or high risk survival groups. Among the2548 fused features, 1695 features showed a statistically significant survivalprobability differences between the two risk groups defined by the extractedfeatures.

Deep Neural Networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer