Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer

Foersch S, Glasner C, Woerl AC, Eckstein M, Wagner DC, Schulz S, Kellers F, Fernandez A, Tserea K, Kloth M, Hartmann A, Heintz A, Weichert W, Roth W, Geppert CI, Kather JN, Jesinghaus M (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Nature Medicine Nature Publishing Group

Book Volume: 29

Pages Range: 430-439

Journal Issue: 2

DOI: 10.1038/s41591-022-02134-1

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.

Authors with CRIS profile

Markus Eckstein Institute of Pathology Arndt Hartmann Lehrstuhl für Allgemeine Pathologie und Pathologische Anatomie Carol-Immanuel Geppert Institute of Pathology

Involved external institutions

Universitätsmedizin der Johannes Gutenberg-Universität Mainz DE Germany (DE) Marienhaus Klinikum Mainz (MKM) / Katholisches Klinikum Mainz DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Universitätsklinikum Aachen (UKA) DE Germany (DE)

How to cite

APA:

Foersch, S., Glasner, C., Woerl, A.-C., Eckstein, M., Wagner, D.-C., Schulz, S.,... Jesinghaus, M. (2023). Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nature Medicine, 29(2), 430-439. https://dx.doi.org/10.1038/s41591-022-02134-1

MLA:

Foersch, Sebastian, et al. "Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer." Nature Medicine 29.2 (2023): 430-439.

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