Development of Explainable Machine Learning Models to Identify Patients at Risk for 1-Year Mortality and New Distant Metastases Postendoprosthetic Reconstruction for Lower Extremity Bone Tumors: A Secondary Analysis of the PARITY Trial.

Journal: JB & JS open access
Published Date: May 22, 2025

Abstract

BACKGROUND: Accurate prediction of postoperative metastasis and mortality risks in patients undergoing lower-limb oncological resection and endoprosthetic reconstruction is essential for guiding adjuvant therapies and managing patient expectations. Current prediction methods are limited by variability in patient-specific factors. This study aims to develop and internally validate explainable machine learning (ML) models to predict the 1-year risk of new distant metastases and mortality in these patients.

Authors

  • Jiawen Deng
    Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada.
  • Myron Moskalyk
    Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Madhur Nayan
    Department of Urology, New York University School of Medicine, New York, NY, United States.
  • Ahmed Aoude
    Orthopaedic Research Laboratory, Research Institute of McGill University Health Centre, Montreal General Hospital, 1650 Cedar Avenue, Montréal, Québec, H3G 1A4, Canada. Electronic address: ahmed.aoude@mcgill.ca.
  • Michelle Ghert
    Division of Orthopaedic Surgery, McMaster University, Hamilton, Ontario, Canada.
  • Sahir Bhatnagar
    McGill University Department of Biostatistics, 805 rue Sherbrooke O, Montréal, H3A 0B9, Quebec, Canada.
  • Anthony Bozzo
    From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg).

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