Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography
This new model suggests the utility of deep learning in PN prediction over traditional pure radiomics and promises better management for patients receiving ICI and the ability to better stratify patients in immunotherapy drug trials.
Authors
- Patrick Cella (ORCID: https://orcid.org/0009-0006-4333-1178)
- Michele L. Lenoue-Newton (ORCID: https://orcid.org/0000-0003-3469-3784)
- Levente Lippenszky (ORCID: https://orcid.org/0009-0000-7835-2071)
- Travis John Osterman (ORCID: https://orcid.org/0000-0002-2841-8121)
- David S. Smith (ORCID: https://orcid.org/0000-0002-1615-2231)
- Christine Micheel (ORCID: https://orcid.org/0000-0002-7744-9039)
- Kathleen F. Mittendorf (ORCID: https://orcid.org/0000-0003-1097-9171)
- Jan Wolber
- Neha Jain (ORCID: https://orcid.org/0000-0002-0168-7874)
Institutions
- National Healthcare Service Center (HU)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1200/cci-24-00198
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44