ArticleJournal of surgical oncology2023
Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.
Article in Journal of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Review
- Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.Cancer research · 2026Article
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- Deep Learning Image Processing Models in Dermatopathology.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology.NPJ digital medicine · 2025Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.Cell reports methods · 2024Article
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
objectivesDeep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction.
methodsH&E-stained slides from The Cancer Genome Atlas (TCGA) colon cohort are passed through the CNN. Resulting imaging features are used to cluster morphologically similar slide regions. Tile-level pairwise similarities are calculated and used to generate a tumor heterogeneity score (THS). Patient-level THS is then correlated with TCGA-reported biomarkers, including MSI-status.
resultsH&E-stained images from 313 patients generated 534 771 tiles. Deep learning automatically identified and annotated cells by type and clustered morphologically similar slide regions. MSI-high tumors demonstrated significantly higher THS than MSS/MSI-low (p < 0.001). THS was higher in MLH1-silent versus non-silent tumors (p < 0.001). The sequencing derived MSIsensor score also correlated with THS (r = 0.51, p < 0.0001).
conclusionsDeep learning provides spatially resolved visualization of imaging-derived biomarkers and automated quantification of tumor heterogeneity. Our novel THS correlates with MSI-status, indicating that with expanded training sets, translational tools could be developed that predict MSI-status using H&E-stained images alone.
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