Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.

Journal: European journal of radiology
PMID: 40096773

Abstract

PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laboratory, and pathological features (including patient age, tumor size, extrathyroidal extension, or serum thyroglobulin levels) are currently used to identify recurrence risk. Validation and potential adjustment of their individual and combined prognostic values using a large patient cohort with several years of follow-up might improve the correct identification of patients at risk.

Authors

  • Andreas Schindele
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Anne Krebold
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Ursula Heiß
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Kerstin Nimptsch
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Elisabeth Pfaehler
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Christina Berr
    Internal Medicine I, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Ralph A Bundschuh
    Department of Nuclear Medicine, University of Bonn Medical Center, Germany.
  • Thomas Wendler
    Technische Universität München, Computer Aided Medical Procedures, Institut für Informatik, I16, Boltzmannstr. 3, Garching bei München 85748, GermanyeSurgicEye GmbH, Friedenstraße 18A, München 81671, Germany.
  • Olivia Kertels
    Institute of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany.
  • Johannes Tran-Gia
    Department of Nuclear Medicine, University Hospital Wuerzburg, Wuerzburg, Germany.
  • Christian H Pfob
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Constantin Lapa
    Nuclear Medicine, Faculty of Medicine, University of Augsburg, Augsburg, Germany. Electronic address: Constantin.lapa@uk-augsburg.de.

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