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Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
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Zamanitajeddin, Neda, Jahanifar, Mostafa, Bilal, Mohsin, Eastwood, Mark and Rajpoot, Nasir M. (2024) Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer. Medical Image Analysis, 93 . 103071. doi:10.1016/j.media.2023.103071 ISSN 1361-8415.
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WRAP-social-network-analysis-cell-networks-improves-deep-learning-prediction-molecular-pathways-key-mutations-colorectal-cancer-2024.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (5Mb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.media.2023.103071
Abstract
Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular pathways, genetic subtypes, and mutations associated with CRC is crucial for precision medicine. However, traditional measurement techniques such as gene sequencing are costly and time-consuming, while most deep learning methods proposed for this task lack interpretability. This study offers a new approach to enhance the state-of-the-art deep learning methods for molecular pathways and key mutation prediction by incorporating cell network information. We build cell graphs with nuclei as nodes and nuclei connections as edges of the network and leverage Social Network Analysis (SNA) measures to extract abstract, perceivable, and interpretable features that explicitly describe the cell network characteristics in an image. Our approach does not rely on precise nuclei segmentation or feature extraction, is computationally efficient, and is easily scalable. In this study, we utilize the TCGA-CRC-DX dataset, comprising 499 patients and 502 diagnostic slides from primary colorectal tumours, sourced from 36 distinct medical centres in the United States. By incorporating the SNA features alongside deep features in two multiple instance learning frameworks, we demonstrate improved performance for chromosomal instability (CIN), hypermutated tumour (HM), TP53 gene, BRAF gene, and Microsatellite instability (MSI) status prediction tasks (2.4%–4% and 7–8.8% improvement in AUROC and AUPRC on average). Additionally, our method achieves outstanding performance on MSI prediction in an external PAIP dataset (99% AUROC and 98% AUPRC), demonstrating its generalizability. Our findings highlight the discrimination power of SNA features and how they can be beneficial to deep learning models’ performance and provide insights into the correlation of cell network profiles with molecular pathways and key mutations.
Item Type: | Journal Article | ||||||||
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Subjects: | H Social Sciences > H Social Sciences (General) Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) R Medicine > RC Internal medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Colon (Anatomy) -- Cancer, Colon (Anatomy) -- Cancer -- Diagnosis -- Data processing, Rectum -- Cancer , Rectum -- Cancer -- Diagnosis -- Data processing, Social sciences -- Network analysis , Deep learning (Machine learning) , Diagnostic imaging, Imaging systems in medicine | ||||||||
Journal or Publication Title: | Medical Image Analysis | ||||||||
Publisher: | Elsevier Science BV | ||||||||
ISSN: | 1361-8415 | ||||||||
Official Date: | April 2024 | ||||||||
Dates: |
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Volume: | 93 | ||||||||
Article Number: | 103071 | ||||||||
DOI: | 10.1016/j.media.2023.103071 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 7 February 2024 | ||||||||
Date of first compliant Open Access: | 7 February 2024 |
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