Generalizable model to predict new or progressing compression fractures in tumor-infiltrated thoracolumbar vertebrae in an all-comer population.

Journal: Journal of neurosurgery. Spine
Published Date:

Abstract

OBJECTIVE: Neurosurgical evaluation is required in the setting of spinal metastases at high risk for leading to a vertebral body fracture. Both irradiated and nonirradiated vertebrae are affected. Understanding fracture risk is critical in determining management, including follow-up timing and prophylactic interventions. Herein, the authors report the results of a machine learning model that predicts the development or progression of a pathological vertebral compression fracture (VCF) in metastatic tumor-infiltrated thoracolumbar vertebrae in an all-comer population.

Authors

  • Alex Flores
    1Department of Neurosurgery.
  • Vijay Nitturi
    2Baylor College of Medicine, Houston; and.
  • Arman Kavoussi
    2Baylor College of Medicine, Houston; and.
  • Max Feygin
    2Baylor College of Medicine, Houston; and.
  • Romulo A Andrade de Almeida
    Departments of3Neurosurgery.
  • Esteban Ramirez Ferrer
    Departments of3Neurosurgery.
  • Adrish Anand
  • Shervin Nouri
    2Baylor College of Medicine, Houston; and.
  • Anthony K Allam
    2Baylor College of Medicine, Houston; and.
  • Ashley Ricciardelli
    2Baylor College of Medicine, Houston; and.
  • Gabriel Reyes
    2Baylor College of Medicine, Houston; and.
  • Sandy Reddy
    2Baylor College of Medicine, Houston; and.
  • Ihika Rampalli
    2Baylor College of Medicine, Houston; and.
  • Laurence Rhines
    Departments of3Neurosurgery.
  • Claudio E Tatsui
    Departments of3Neurosurgery.
  • Robert Y North
    Departments of3Neurosurgery.
  • Amol Ghia
    4Radiation Oncology, and.
  • Jeffrey H Siewerdsen
    Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD, USA.
  • Alexander E Ropper
    1Department of Neurosurgery.
  • Christopher Alvarez-Breckenridge
    Departments of3Neurosurgery.

Keywords

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