Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules
Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.
Authors
- David M. DiBardino (ORCID: https://orcid.org/0000-0003-2524-1348)
- Andrew Julian Vickers (ORCID: https://orcid.org/0000-0003-1525-6503)
- Katharine A. Rendle (ORCID: https://orcid.org/0000-0002-7761-8728)
- Roger Y. Kim (ORCID: https://orcid.org/0000-0002-8262-484X)
- Anil Vachani (ORCID: https://orcid.org/0000-0002-3871-8697)
- Nandita Mitra (ORCID: https://orcid.org/0000-0002-7714-3910)
- J. Steltz (ORCID: https://orcid.org/0000-0002-1810-1745)
- L. Pickup (ORCID: https://orcid.org/0000-0003-3183-297X)
- Sana Zeb (ORCID: https://orcid.org/0009-0003-5675-5525)
- Clarisa Yee
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- NYU Langone Health (US)
- Pulmonary and Allergy Associates (US)
- Shell (Netherlands) (NL)
- University of Pennsylvania (US)
Publication Details
- Journal
- JNCI Cancer Spectrum
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1093/jncics/pkae086
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- U.S. Department of Health and Human Services
- American Thoracic Society
- National Cancer Institute