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

Foundations of AI (47) AI Data (13) Applications of AI (104)

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 (2) Tweetations (3) Metrics

Journals

  1. Vrettos K, Triantafyllou M, Marias K, Karantanas A, Klontzas M. Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence. BJR|Artificial Intelligence 2024;1(1) View
  2. Tabataba Vakili S, Haywood D, Kirk D, Abdou A, Gopalakrishnan R, Sadeghi S, Guedes H, Tan C, Thamm C, Bernard R, Wong H, Kuhn E, Kwan J, Lee S, Hart N, Paterson C, Chopra D, Drury A, Zhang E, Raeisi Dehkordi S, Ashbury F, Kotronoulas G, Chow E, Jefford M, Chan R, Fazelzad R, Raman S, Alkhaifi M. Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review. JCO Clinical Cancer Informatics 2024;(8) View