Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Open accessAbstract
BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.
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Loki Annotate was applied to four benchmark histopathology datasets including CRC7K (eight tissue types), WSSS-4LUAD (normal and tumor), PatchCamelyon (normal and tumor) and LC25000 (benign and malignant).
“We applied Loki Annotate to four benchmark histopathology datasets including CRC7K41 (eight tissue types), WSSS-4LUAD42 (normal and tumor), PatchCamelyon43 (normal and tumor) and LC2500044 (benign and malignant).”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [41]
Prognostic AI models for colorectal cancer have been developed for MRI, histopathology, and multiplex imaging.
“Prognostic AI models for colorectal cancer have been developed for various data modalities, including MRI (88), histopathology (89-91), and multiplex imaging (92,93).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [90]
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- Authors
- Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., Jansen, L., Reyes-Aldasoro, C. C., Zörnig, I., Jäger, D., Brenner, H., Chang-Claude, J., Hoffmeister, M., & Halama, N.
- Affiliations
- German Cancer Research Center (DE)
- University Hospital Heidelberg (DE)
- Universitätsklinikum Aachen (DE)
- Heidelberg University (DE)
- University Medical Centre Mannheim (DE)
- Corresponding author
- Kather, Jakob Nikolas
- ORCID
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-01-24
- Volume
- 16
- Issue
- 1
- Pages
- e1002730
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC6345440
- OpenAlex
- W2914568698
- Cited by
- 1,082
- References
- 52
- Citations by year
- 2026: 158 · 2025: 164 · 2024: 180 · 2023: 170 · 2022: 175 · 2021: 129 · 2020: 87 · 2019: 17 · 2014: 1
- FWCI
- 46.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- City Research Online (City University London) (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- RWTH Publications (RWTH Aachen)
- Funders
- Deutsches Krebsforschungszentrum (Heidelberg School of Oncology)
- Bundesministerium für Bildung und Forschung (13GW0091E, 01ER0814, 01ER0815, 01ER1505A, 01ER1505B)
- Deutsche Forschungsgemeinschaft (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, BR 1704/17-1, CH 117/1-1, and HO 5117/2-1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Keywords
- colorectal cancer, deep convolutional neural networks, tumor stroma, prognostic factors, overall survival, cancer-associated fibroblasts, cancer of unknown primary, relapse-free survival, TCGA, transfer learning, histopathology image analysis, digital pathology
- MeSH terms
- Deep Learning, Colon, Coloring Agents, Eosine Yellowish-(YS), Female, Hematoxylin, Humans, Image Interpretation, Computer-Assisted, Male, Prognosis, Rectum, Retrospective Studies, Colorectal Neoplasms
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