Development of Machine Learning Models for Predicting the 1-Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial.

Journal: Journal of surgical oncology
Published Date: Sep 11, 2024

Abstract

BACKGROUND: Oncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoperation risk.

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.
  • Matthew Shammas-Toma
    Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
  • 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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