A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Apr 29, 2025

Abstract

BACKGROUND AND PURPOSE: The most common adverse event following spine stereotactic body radiotherapy (SBRT) is vertebral compression fracture (VCF). There is interest in the development of patient-specific tools that can predict those at high risk of developing VCF. This study aimed to develop a machine learning tool able to predict the development of VCF following spine SBRT using clinical, dosimetric and tumor risk factors.

Authors

  • Laura Burgess
    Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. Electronic address: laburgess@toh.ca.
  • Matthew Rezkalla
    Holland Bone and Joint Program, Sunnybrook Research Institute, Toronto, ON, Canada.
  • Geoffrey Klein
    Holland Bone and Joint Program, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
  • Batuhan Karagoz
    Engineering Science, Faculty of Engineering, University of Toronto, ON, Canada.
  • Gonzalo Martinez Santos
    Engineering Science, Faculty of Engineering, University of Toronto, ON, Canada.
  • Mobin Malmirian
    Engineering Science, Faculty of Engineering, University of Toronto, ON, Canada.
  • Cari Whyne
    Division of Orthopaedic Surgery, University of Toronto, Toronto, ON, Canada; Granovsky Gluskin Division of Orthopaedics, Mount Sinai Hospital, Toronto, ON, Canada; Division of Orthopaedic Surgery, Sunnybrook Health Science Centre, Toronto, ON, Canada.
  • Arjun Sahgal
  • Michael Hardisty

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