Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer
Abstract
Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 patients with CRC. Our model had high prognostic capabilities and outperformed other clinical, molecular and immune cell-based parameters. It could also be used to predict the response to neoadjuvant therapy in patients with rectal cancer. Using an explainable AI approach, we confirmed that the MSDLM's decisions were based on established cellular patterns of anti-tumor immunity. Hence, the AIS could provide clinicians with a valuable decision-making tool based on the tumor immune microenvironment.
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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 [93]
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- Authors
- Foersch, S., Glasner, C., Woerl, A.-C., Eckstein, M., Wagner, D.-C., Schulz, S., Kellers, F., Fernandez, A., Tserea, K., Kloth, M., Hartmann, A., Heintz, A., Weichert, W., Roth, W., Geppert, C., Kather, J. N., & Jesinghaus, M.
- Affiliations
- University Medical Center of the Johannes Gutenberg University Mainz (DE)
- Johannes Gutenberg University Mainz (DE)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Technical University of Munich (DE)
- University of Leeds (GB)
- Universitätsklinikum Gießen und Marburg (DE)
- Corresponding author
- Foersch, Sebastian
- ORCID
- Venue
- Nature Medicine
- Published
- 2023-01-09
- Volume
- 29
- Issue
- 2
- Pages
- 430-439
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4313830579
- Semantic Scholar
- 2378cf8bb6fb4a9c4cde69d40d423a2b963b78ff
- Cited by
- 251
- References
- 46
- Citations by year
- 2026: 46 · 2025: 80 · 2024: 89 · 2023: 36
- FWCI
- 23.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (16SV8167)
- Stage-I-Program of the University Medical Center Mainz Mainz Research School of Translational Biomedicine (TransMed) Manfred-Stolte-Foundation
- Bundesministerium für Gesundheit (ZMVI1-2520DAT111)
- Deutsche Krebshilfe (#70113864)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- colorectal cancer, tumor immune microenvironment, immune cell composition, outcome prediction, neoadjuvant therapy response, rectal cancer, explainable AI, antitumor immunity, immune score
- MeSH terms
- Deep Learning, Artificial Intelligence, Humans, Rectal Neoplasms, Colorectal Neoplasms, Tumor Microenvironment
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