ArticleNPJ precision oncology2024
Novel radiogenomics approach to predict and characterize pneumonitis in stage III NSCLC.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- [Advances in Radiomics for Immune Checkpoint Inhibitor-related Pneumonitis of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Artificial intelligence and radiomics in drug-induced interstitial lung disease.ERJ open research · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Radiation Dose-Dependent and -Independent Pulmonary Infiltrates in Patients with High-Grade Pneumonitis After Radiochemotherapy and Durvalumab Consolidation for Stage III NSCLC.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Imaging-based transformer model predicts early therapy response in advanced nasopharyngeal carcinoma: a dual-center study.Insights into imaging · 2025Article
- [Risk Factors, Molecular Mechanisms, and Multimodal Early Warning Strategies for Immune Checkpoint Inhibitor-associated Pneumonitis in Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Review
- [Combination of Radiotherapy and Immunotherapy for Unresectable Stage III Non-small Cell Lung Cancer: Clinical Progress Advances and Future Directions].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
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.
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Registered trials
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