Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC
Unresectable stage III NSCLC is now treated with chemoradiation (CRT) followed by immune checkpoint inhibitors (ICI). Pneumonitis, a common CRT complication, has heightened risk with ICI, potentially causing severe outcomes. Currently, there are no biomarkers to predict pneumonitis risk or differentiate between radiation-induced pneumonitis (RTP) and ICI-induced pneumonitis (IIP). This study analyzed 293 patients from two institutions, with 140 experiencing pneumonitis (RTP: 84, IIP: 56). Two models were developed: M1 predicted pneumonitis risk using seven radiomic features, achieving high accuracy across internal and external datasets (AUCs: 0.76 and 0.85). M2 differentiated RTP from IIP, with strong performance (AUCs: 0.86 and 0.81). Gene set enrichment analysis linked high pneumonitis risk to pathways such as ECM-receptor interaction and T-cell signaling, while high IIP risk correlated with MAPK and JAK-STAT pathways. Radiomic models show promise in early pneumonitis risk stratification and distinguishing pneumonitis types, potentially guiding personalized NSCLC treatment.
Authors
- Nathan A. Pennell (ORCID: https://orcid.org/0000-0002-1458-0064)
- Anant Madabhushi (ORCID: https://orcid.org/0000-0002-5741-0399)
- Pradnya D. Patil (ORCID: https://orcid.org/0000-0003-1304-3678)
- Mohammadhadi Khorrami (ORCID: https://orcid.org/0000-0002-4141-8548)
- Lukas Delasos (ORCID: https://orcid.org/0000-0002-6409-2640)
- Amit Banwari Gupta (ORCID: https://orcid.org/0000-0001-5345-6763)
- Khalid Jazieh (ORCID: https://orcid.org/0000-0002-3981-4293)
- Kevin L. Stephans (ORCID: https://orcid.org/0000-0002-5554-8474)
- Kristin Ann Higgins (ORCID: https://orcid.org/0000-0003-1496-9878)
- Yifu Ding (ORCID: https://orcid.org/0000-0001-7779-7781)
- Vidya Sankar Viswanathan
- Pushkar Mutha (ORCID: https://orcid.org/0000-0002-5961-7131)
Institutions
- Georgia Institute of Technology (US)
- Cleveland Clinic (US)
- Emory University (US)
- University Hospitals of Cleveland (US)
- Veterans Health Administration (US)
- Atlanta VA Medical Center (US)
- Covance (United States) (US)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1038/s41698-024-00790-9
- Citations
- 10
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.04
Funders
- U.S. Department of Defense
- Foundation for the National Institutes of Health
- U.S. Department of Veterans Affairs
- Eli Lilly and Company
- AstraZeneca
- California Breast Cancer Research Program
- National Institutes of Health
- National Heart, Lung, and Blood Institute
- National Cancer Institute
- National Institute of Biomedical Imaging and Bioengineering
- National Center for Research Resources
- DOD Prostate Cancer Research Program
- DOD Peer Reviewed Cancer Research Program