Machine learning-based models for outcome prediction in skull base and spinal chordomas: a systematic review and meta-analysis.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Published Date: Jun 14, 2025

Abstract

BACKGROUND: Chordomas are primary bone lesions originating from embryonic notochord remnants, most commonly developing along the skull base and spine. Managing chordomas is challenging due to the complex surgical approaches and significant resistance to chemotherapy and radiation. Consequently, the prognosis for chordoma treatment is unfavorable. We aimed to systematically assess the outcomes of machine learning (ML) models in predicting progression, recurrence, and survival in chordoma patients.

Authors

  • Bardia Hajikarimloo
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ibrahim Mohammadzadeh
    Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: Ibrahim.mdz7777@gmail.com.
  • Azin Ebrahimi
    Department of Neurological Surgery, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Salem M Tos
    Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA.
  • Rana Hashemi
    Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences, Tehran, Iran.
  • Arman Hasanzade
    Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Mohammad Amin Habibi
    Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.

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