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](https://asset.jmir.pub/assets/f702bc12e04390b4b4bed319f283dbd4.png 480w,https://asset.jmir.pub/assets/f702bc12e04390b4b4bed319f283dbd4.png 960w,https://asset.jmir.pub/assets/f702bc12e04390b4b4bed319f283dbd4.png 1920w,https://asset.jmir.pub/assets/f702bc12e04390b4b4bed319f283dbd4.png 2500w)
Journals
- 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