ArticleAmerican journal of cancer research2023
Comprehensive nomogram models for predicting checkpoint inhibitor pneumonitis.
Article in American journal of cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 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
- Spatial immune profiling of local tissues and its correlation with peripheral blood cytokines in lung squamous cell carcinoma patients with immune-related pneumonitis.Translational cancer research · 2026Article
- Nomogram-based prediction of checkpoint inhibitor pneumonitis in lung cancer patients.BMC cancer · 2026Article
- Impact of proton pump inhibitor use on immune-related adverse events in patients with non-small cell lung cancer: a retrospective study.BMC cancer · 2026Article
- Novel circulating microRNA signature for early detection and prognostication of checkpoint inhibitor-related pneumonitis.Journal for immunotherapy of cancer · 2025Article
- Heterogeneity of Checkpoint Inhibitor-Associated Pneumonitis: A Multicenter Study on Inflammatory Subtypes and Clinical Outcomes.Cancer medicine · 2025Article
- Expert consensus on cancer treatment-related lung injury.Journal of thoracic disease · 2025Review
- The application of bronchoscopy in the assessment of immune checkpoint inhibitor-related pneumonitis severity and recurrence.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Checkpoint inhibitor pneumonitis (CIP) is a common type of immune-related adverse events (irAEs) with poor clinical prognosis. Currently, there is a lack of effective biomarkers and predictive models to predict the occurrence of CIP. This study retrospectively enrolled 547 patients who received immunotherapy. The patients were divided into CIP cohorts of any grade, or grade ≥2 or ≥3. Multivariate logistic regression analysis was used to determine the independent risk factors, based on which we established Nomogram A and B for respectively predicting any grade or grade ≥2 CIP. For Nomogram A to predict any grade CIP, the C indexes in the training and validation cohorts were 0.827 (95% CI=0.772-0.881) and 0.860 (95% CI=0.741-0.918), respectively. Similarly, for Nomogram B to predict grade 2 or higher CIP, the C indexes of the training and validation cohorts were 0.873 (95% CI=0.826-0.921) and 0.904 (95% CI=0.804-0.973), respectively. In conclusion, the predictive power of nomograms A and B has proven satisfactory following internal and external verification. They are promising clinical tools that are convenient, visual, and personalized for assessing the risks of developing CIP.
Indexed as
Identifiers
37424813PMC10326584W4383710291What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.