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

arXiv: https://arxiv.org/abs/2212.06834 · HTML: https://mlsanity.com/paper/2212.06834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Underspecification Presents Challenges for Credibility in Modern Machine Learning](https://mlsanity.com/paper/2011.03395/) (2020)
- [Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding](https://mlsanity.com/paper/1606.01847/) (2016)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Interpretable Survival Prediction for Colorectal Cancer using Deep Learning](https://mlsanity.com/paper/2011.08965/) (2020)
- [A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction](https://mlsanity.com/paper/2201.10353/) (2022)

## Built on by (most cited first, 1 of 1)

- [Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and Omics Data for Precision Oncology](https://mlsanity.com/paper/2411.17418/) (2024)

## Abstract (arXiv)

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.