ArticleFrontiers in immunology2023
Identification and prediction of immune checkpoint inhibitors-related pneumonitis by machine learning.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 18 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
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape.Human vaccines & immunotherapeutics · 2026Article
- Serum albumin is associated with immune-related hypothyroidism and treatment continuity in non-small cell lung cancer.International journal of clinical oncology · 2026Article
- CT-based deep foundation model for predicting immune checkpoint inhibitor-induced pneumonitis risk in lung cancer.Journal for immunotherapy of cancer · 2026Article
- [Advances in Radiomics for Immune Checkpoint Inhibitor-related Pneumonitis of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Efficacy and safety of deep hyperthermia combined with tislelizumab and chemotherapy in the treatment of advanced squamous non-small-cell lung cancer: a single-center retrospective study.Frontiers in oncology · 2026Article
- Development and external validation of an interpretable deep learning model for early immune-related adverse events in hepatocellular carcinoma.Frontiers in immunology · 2026Article
- Development and validation of a multidimensional machine-learning model to predict clinically significant immune-related adverse events in advanced non-small cell lung cancer.American journal of translational research · 2026Article
- Risk factors and mechanisms of immune checkpoint inhibitor-related pneumonitis.Human vaccines & immunotherapeutics · 2025Review
- Heterogeneous graph neural network-based prediction of immune-related adverse events.Scientific reports · 2025Article
- Article
- Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.JCO clinical cancer informatics · 2025Article
- Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.JCO clinical cancer informatics · 2024Article
- Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.Frontiers in digital health · 2024Article
- Durvalumab-Associated Pneumonitis in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Real-World Population Study.Current oncology (Toronto, Ont.) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Immune checkpoint inhibitor (ICI)-related pneumonitis (IRP) is a common and potentially fatal clinical adverse event. The identification and prediction of the risk of ICI-related IRP is a major clinical issue. The objective of this study was to apply a machine learning method to explore risk factors and establish a prediction model. Methods: We retrospectively analyzed 48 patients with IRP (IRP group) and 142 patients without IRP (control group) who were treated with ICIs. An Elastic Net model was constructed using a repeated k-fold cross-validation framework (repeat = 10; k = 3). The prediction models were validated internally and the final prediction model was built on the entire training set using hyperparameters with the best interval validation performance. The generalizability of the final prediction model was assessed by applying it to an independent test set. The overall performance, discrimination, and calibration of the prediction model were evaluated. Results: Eleven predictors were included in the final predictive model: sindillizumab, number of ≥2 underlying diseases, history of lung diseases, tirelizumab, non-small cell lung cancer (NSCLC), percentage of CD4 Conclusion: The prediction model of ICI-related IRP provides a tool for accurately predicting the occurrence of IRP in patients with cancer who received ICIs.
Indexed as
Identifiers
What 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.