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

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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.
Funding Information
  • Bundesministerium für Forschung, Technologie und Raumfahrt (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)

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