Deep Neural Networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer
This work addresses improved staging and treatment outcomes for colon cancer patients by integrating multi-modal data, though it is incremental as it builds on existing deep learning methods.
The researchers tackled the problem of diverse variation in colon cancer stages by integrating genomics and histopathological images into an ensemble deep neural network, resulting in an AUC ROC of 0.95 for stage classification compared to 0.71 and 0.68 with individual features, and identifying 1695 significant features for survival risk stratification.
There exists unexplained diverse variation within the predefined colon cancer stages using only features either from genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved in staging and treatment outcome, hence motivated by the advancement of Deep Neural Network libraries and different structures and factors within some genomic dataset, we aggregate atypical patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA Methylation as an integrative input source into an ensemble deep neural network for colon cancer stages classification and samples stratification into low or high risk survival groups. The results of our Ensemble Deep Convolutional Neural Network model show an improved performance in stages classification on the integrated dataset. The fused input features return Area under curve Receiver Operating Characteristic curve (AUC ROC) of 0.95 compared with AUC ROC of 0.71 and 0.68 obtained when only genomics and images features are used for the stage's classification, respectively. Also, the extracted features were used to split the patients into low or high risk survival groups. Among the 2548 fused features, 1695 features showed a statistically significant survival probability differences between the two risk groups defined by the extracted features.