Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Open accessAbstract
Background Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning.Methods More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival.Findings 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72-5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07-4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion.Interpretation A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes.Funding The Research Council of Norway.
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Histopathology whole slide image provides information that reflects molecular characteristics or genetic patterns.
“Emerging evidence has shown that histopathology whole slide image (WSI) provides information that reflects molecular characteristics or genetic patterns.”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [14]
WSI-based deep learning can improve efficiency in predicting prognosis and treatment response.
“WSI-based deep learning can improve efficiency in predicting prognosis and treatment response fields (14–16).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [14]
AI-driven whole slide pathology analysis has been applied in diagnosis, prognosis, and efficacy prediction.
“Artificial intelligence (AI)–driven whole slide pathology analysis has been applied in diagnosis, prognosis, and efficacy prediction, and it has become a research hotspot (14, 24, 25).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [14]
The fundamental criterion for evaluating a medical AI agent is its precision in generating diagnostic and treatment recommendations.
“The fundamental criterion for evaluating a medical AI agent is its precision in generating diagnostic and treatment recommendations.80,100,118,119,120”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [119]
Several validation studies of histopathology-based prognostic models for colorectal cancer exist, including one showing a prognostic feature previously identified through AI can be learned and used by pathologists.
“There have additionally been several validation studies of histopathology-based prognostic models (95,96), including a study demonstrating that a prognostic feature previously identified through AI analysis can be learned and used by pathologists (95).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [96]
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- Authors
- Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., Farstad, I. N., Domingo, E., Church, D. N., Nesbakken, A., Shepherd, N. A., Tomlinson, I., Kerr, R., Novelli, M., Kerr, D. J., & Danielsen, H. E.
- Affiliations
- Oslo University Hospital (NO)
- University of Oxford (GB)
- Centre for Human Genetics (GB)
- Cheltenham General Hospital (GB)
- ORCID
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 395
- Issue
- 10221
- Pages
- 350-360
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W3004053956
- Cited by
- 627
- References
- 31
- Citations by year
- 2026: 67 · 2025: 97 · 2024: 108 · 2023: 119 · 2022: 115 · 2021: 92 · 2020: 28
- FWCI
- 34.77
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- The Lancet (publishedVersion)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
- Funders
- IKTPLUSS Lighthouse (259204)
- 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 Surgical Treatments
- Keywords
- colorectal cancer, prognostic factors, deep learning, convolutional neural networks, H&E staining, digital pathology, cancer-specific survival, adjuvant therapy selection, stage II and III colorectal cancer, capecitabine, formalin-fixed paraffin-embedded tissue, patient stratification
- MeSH terms
- Deep Learning, Aged, Eosine Yellowish-(YS), Female, Hematoxylin, Humans, Male, Middle Aged, Prognosis, Prospective Studies, Biomarkers, Tumor, Colorectal Neoplasms, Cohort Studies, Kaplan-Meier Estimate, Early Detection of Cancer
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