ArticleiScience2026
Leveraging serum CHI3L1 and machine learning for non-invasive liver fibrosis diagnostics.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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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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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was aimed to develop non-invasive liver fibrosis diagnostic model using chitinase 3-like protein 1 (CHI3L1) based on machine learning (ML).1,011 follow-up patients (556 training/455 validation) were enrolled, staging fibrosis via transient elastography (F01, F2, F23, F34, and F4). Demographic, clinical, and laboratory parameters were collected. Fibrosis-related indicators were selected by differential analysis and correlation analysis. ML models were developed, optimized, and validated. Of 23 initial laboratory indicators, 15 indicators were selected to developed a 5-stage liver fibrosis diagnostic model (XG-Boost, FIB5-15, area under the curve [AUC] = 0.93, sensitivity = 0.70, specificity = 0.93). This was refined to a 7-indicator model (FIB5-7, area under the ROC curve [AUC] = 0.98). By merging F2/F23 and F34/F4, a 3-category optimized model (FIB3-7, AUC = 0.94) and a 2-category model (FIB2-7, AUC = 0.95) were created to identify reversible fibrosis. The final FIB2-7 model significantly outperformed aspartate aminotransferase‑to‑platelet ratio index (APRI) and FIB-4 (
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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.