Ogundipe, O., Kurt, Z. orcid.org/0000-0003-3186-8091 and Woo, W.L. (2024) Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer. PLOS ONE, 19 (9). e0305268. ISSN 1932-6203
Abstract
Motivation
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.
Results
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
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 Ogundipe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Cancer genomics; Breast cancer; Colorectal cancer; Cancers and neoplasms; Gastrointestinal cancers; Cancer treatment; Genomics; Lung and intrathoracic tumors |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Sep 2024 10:55 |
Last Modified: | 04 Sep 2024 10:55 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0305268 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216814 |