ArticleBMC cancer2024
Quantification of preexisting lung ground glass opacities on CT for predicting checkpoint inhibitor pneumonitis in advanced non-small cell lung cancer patients.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.
- Diagnostic research in immune checkpoint inhibitor-related pneumonitis: a bibliometric analysis of research evolution, diagnostic focuses, and future priorities.Frontiers in oncology · 2026Pooled it
- Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- 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
- Interactions between interstitial lung abnormalities and immune checkpoint inhibitor therapy in non-small cell lung cancer: A review of current understanding and future directions.Human vaccines & immunotherapeutics · 2025Review
- [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
- Analysis of baseline interstitial lung abnormality on the risk of checkpoint inhibitor-related pneumonitis and survival in advanced non-small cell lung cancer patients treated with first-line PD-1/PD-L1 inhibitors.Translational lung cancer research · 2025Article
- The impact of pre-existing interstitial lung disease on radiation and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.Therapeutic advances in medical oncology · 2025Article
- First-line chemoimmunotherapy for patients with small-cell lung cancer and interstitial lung abnormality: CIP risk and prognostic analysis.Thoracic cancer · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
backgroundImmune checkpoint inhibitors (ICIs) can lead to life-threatening pneumonitis, and pre-existing interstitial lung abnormalities (ILAs) are a risk factor for checkpoint inhibitor pneumonitis (CIP). However, the subjective assessment of ILA and the lack of standardized methods restrict its clinical utility as a predictive factor. This study aims to identify non-small cell lung cancer (NSCLC) patients at high risk of CIP using quantitative imaging.
methodsThis cohort study involved 206 cases in the training set and 111 cases in the validation set. It included locally advanced or metastatic NSCLC patients who underwent ICI therapy. A deep learning algorithm labeled the interstitial lesions and computed their volume. Two predictive models were developed to predict the probability of grade ≥ 2 CIP or severe CIP (grade ≥ 3). Cox proportional hazard models were employed to analyze predictors of progression-free survival (PFS).
resultsIn a training cohort of 206 patients, 21.4% experienced CIP. Two models were developed to predict the probability of CIP based on different predictors. Model 1 utilized age, histology, and preexisting ground glass opacity (GGO) percentage of the whole lung to predict grade ≥ 2 CIP, while Model 2 used histology and GGO percentage in the right lower lung to predict grade ≥ 3 CIP. These models were validated, and their accuracy was assessed. In another exploratory analysis, the presence of GGOs involving more than one lobe on pretreatment CT scans was identified as a risk factor for progression-free survival.
conclusionsThe assessment of GGO volume and distribution on pre-treatment CT scans could assist in monitoring and manage the risk of CIP in NSCLC patients receiving ICI therapy. CLINICAL RELEVANCE STATEMENT: This study's quantitative imaging and computational analysis can help identify NSCLC patients at high risk of CIP, allowing for better risk management and potentially improved outcomes in those receivingICI treatment.
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Registered trials
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