A Scalable Radiomics- and Natural Language Processing-Based Machine Learning Pipeline to Distinguish Between Painful and Painless Thoracic Spinal Bone Metastases: Retrospective Algorithm Development and Validation Study.

Journal: JMIR AI
Published Date:

Abstract

BACKGROUND: The identification of objective pain biomarkers can contribute to an improved understanding of pain, as well as its prognosis and better management. Hence, it has the potential to improve the quality of life of patients with cancer. Artificial intelligence can aid in the extraction of objective pain biomarkers for patients with cancer with bone metastases (BMs).

Authors

  • Hossein Naseri
    Medical Physics Unit, McGill University Health Centre, Montreal, QC, Canada.
  • Sonia Skamene
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • Marwan Tolba
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • Mame Daro Faye
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • Paul Ramia
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • Julia Khriguian
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • Marc David
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • John Kildea
    Medical Physics Unit, McGill University Health Centre, Montreal, QC, Canada.

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