This paper is in the following e-collection/theme issue:

Artificial Intelligence (122) AI Data (2) Digital Pain Assessment and Management (152) Machine Learning (1401) Nails and Musculoskeletal (18)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44779, first published .
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

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

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

Authors of this article:

Hossein Naseri1 Author Orcid Image ;   Sonia Skamene2 Author Orcid Image ;   Marwan Tolba2 Author Orcid Image ;   Mame Daro Faye2 Author Orcid Image ;   Paul Ramia2 Author Orcid Image ;   Julia Khriguian2 Author Orcid Image ;   Marc David2 Author Orcid Image ;   John Kildea1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Hossein Naseri   1 , MSc ;   Sonia Skamene   2 , MD ;   Marwan Tolba   2 , MD ;   Mame Daro Faye   2 , MD ;   Paul Ramia   2 , MD ;   Julia Khriguian   2 , MD ;   Marc David   2 , MD ;   John Kildea   1 , PhD

1 Medical Physics Unit, McGill University Health Centre, Montreal, QC, Canada

2 Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada

Corresponding Author:

  • Hossein Naseri, MSc
  • Medical Physics Unit
  • McGill University Health Centre
  • Cedars Cancer Centre
  • 1001 boul Décarie Montréal
  • Montreal, QC, H4A 3J1
  • Canada
  • Phone: 1 514-934-1934 ext 44158
  • Email: 3naseri@gmail.com