ArticleJGH open : an open access journal of gastroenterology and hepatology2025
Development of a Predictive Model for Classifying Immune Checkpoint Inhibitor-Induced Liver Injury Types.
Article in JGH open : an open access journal of gastroenterology and hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Prediction of Immune Checkpoint Inhibitor-Induced Liver Injury in Patients with Gastrointestinal Cancer: Machine Learning Modeling for Time-Stratified Risk Assessment.Current oncology (Toronto, Ont.) · 2026Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; however, they are associated with ICI-induced liver injury (ICI-LI), which manifests as hepatocellular, mixed, or cholestatic patterns with variable treatment responses. This study aimed to develop and validate a predictive model to identify ICI-LI type using clinical data available at ICI initiation. Methods: A retrospective analysis of 297 patients with ICI-LI was conducted. Baseline clinical data were analyzed using univariate and multivariate logistic regression to predict ICI-LI types in the training and validation cohorts. A predictive model was developed and validated using receiver operating characteristic (ROC) curve analysis. Results: Multivariate analysis in the training cohort identified male sex (odds ratio [OR]: 3.33, 95% confidence interval [CI]: 1.57-7.06, Conclusion: This predictive model demonstrates its utility in classifying ICI-LI types.
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