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

• Published in: PloS one Vol. 19; no. 9; p. e0305268
• Main Authors: Ogundipe, Olalekan, Kurt, Zeyneb, Woo, Wai Lok
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.09.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Artificial neural networks; Biology and Life Sciences; Carcinogens; Care and treatment; Classification; Colon cancer; Colonic Neoplasms - genetics; Colonic Neoplasms - mortality; Colonic Neoplasms - pathology; Colorectal cancer; Datasets; Deep Learning; Diagnosis; DNA Methylation; Feature extraction; Gene expression; Genomic analysis; Genomics; Genomics - methods; Histology, Pathological; Humans; Image processing; Medical imaging; Medicine and Health Sciences; MicroRNA; MicroRNAs; MicroRNAs - genetics; miRNA; mRNA; Neoplasm Staging; Neural networks; Neural Networks, Computer; Prognosis; Risk; ROC Curve; Statistical analysis; Survival; United Kingdom
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0305268

There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved staging and treatment outcomes. Hence, motivated by the advancement of Deep Neural Network (DNN) libraries and complementary factors within some genomics datasets, we aggregate atypia patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA methylation as an integrative input source into a deep neural network for colon cancer stages classification, and samples stratification into low or high-risk survival groups. The genomics-only and integrated input features return Area Under Curve-Receiver Operating Characteristic curve (AUC-ROC) of 0.97 compared with AUC-ROC of 0.78 obtained when only image features are used for the stage's classification. A further analysis of prediction accuracy using the confusion matrix shows that the integrated features have a weakly improved accuracy of 0.08% more than the accuracy obtained with genomics features. Also, the extracted features were used to split the patients into low or high-risk survival groups. Among the 2,700 fused features, 1,836 (68%) features showed statistically significant survival probability differences in aggregating samples into either low or high between the two risk survival groups. Availability and Implementation: https://github.com/Ogundipe-L/EDCNN.