Advancing breast, lung and prostate cancer research with federated learning. A systematic review.

Journal: NPJ digital medicine
Published Date: May 27, 2025

Abstract

Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative training of machine learning (ML) models on diverse, multi-centre data. This systematic review synthesises current knowledge on state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Unlike previous surveys, we critically evaluate FL's real-world implementation and impact, demonstrating its effectiveness in enhancing ML generalisability and performance in clinical settings. Our analysis reveals that FL outperformed centralised ML in 15 out of 25 studies, spanning diverse models and clinical applications, including multi-modal integration for precision medicine. Despite challenges identified in reproducibility and standardisation, FL demonstrates substantial potential for advancing cancer research. We propose future research focus on addressing these limitations and investigating advanced FL methods to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.

Authors

  • Anshu Ankolekar
    Department of Precision Medicine, GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands. a.ankolekar@maastrichtuniversity.nl.
  • Sebastian Boie
    Pfizer Pharma GmbH, Berlin, Germany.
  • Maryam Abdollahyan
    Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
  • Emanuela Gadaleta
    Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
  • Seyed Alireza Hasheminasab
    Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Charles Beauville
    Flower Labs, Hamburg, Germany.
  • Nikolaos Dikaios
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • George Anthony Kastis
    Mathematics Research Center, Academy of Athens, Athens, Greece.
  • Michael Bussmann
    Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
  • Claude Chelala
    Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom.
  • Sara Khalid
    Center for Statistics in Medicine, Botnar Research Center, University of Oxford, Oxford, UK. Electronic address: sara.khalid@ndorms.ox.ac.uk.
  • Hagen Kruger
    Pfizer Pharma GmbH, Berlin, Germany.
  • Philippe Lambin
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Giorgos Papanastasiou
    Edinburgh Imaging Facility QMRI, Edinburgh, EH16 4TJ, UK; Centre for Cardiovascular Science, Edinburgh, EH16 4TJ, UK.

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