Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival.

Journal: World neurosurgery
Published Date: Nov 1, 2018

Abstract

BACKGROUND: Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learning studies in the chordoma literature. The purpose of this study was to develop machine learning models for survival prediction and deploy them as open access web applications as a proof of concept for machine learning in rare nervous system lesions.

Authors

  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Quirina Thio
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Paul Ogink
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Jason Kim
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Santiago Lozano-Calderon
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Kevin Raskin
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.

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