Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma.

Journal: BJC reports
Published Date: May 20, 2025

Abstract

BACKGROUND: The heterogeneous and aggressive nature of diffuse large B-cell lymphoma (DLBCL) presents significant treatment challenges as up to 50% of patients experience recurrence of disease after chemotherapy. Upfront detection of recurring patients could offer alternative treatments. Deep learning has shown potential in predicting recurrence of various cancer types but suffers from lack of interpretability. Particularly in prediction of recurrence, an understanding of the model's decision could eventually result in novel treatments.

Authors

  • Hussein Naji
    Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
  • Paul Hahn
    Informatics, TU Darmstadt, Germany.
  • Juan I Pisula
    Centre for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
  • Stefano Ugliano
    Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
  • Adrian Simon
    Institute of Pathology, University Hospital Cologne, Cologne, Germany.
  • Reinhard Büttner
    Institute of Pathology, University Hospital Cologne, Cologne, Germany.
  • Katarzyna Bozek
    Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.

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