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Explainable machine learning incorporating CHI3L1 enhances liver fibrosis staging in chronic hepatitis B: The CHILI
Qingxian Cai1, Jia Liu2, Wenfang Xu3
1Department of Hepatopathy, the Third People's Hospital of Shenzhen, the Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen 518112, PR China.
Background:
Identifying significant liver fibrosis (≥F2) is critical for initiating antiviral therapy in chronic hepatitis B (CHB). Chitinase-3-like protein 1 (CHI3L1) is a promising fibrosis biomarker, but its staging value remains unclear. We evaluated CHI3L1 (chemiluminescent immunoassay, CLIA) alone and with machine learning (ML) for differentiating mild (F0-F1) from significant (F2-F4) fibrosis.
Methods:
We included 347 participants (303 primary, 44 external validation), most CHB patients with biopsy-confirmed METAVIR staging. Serum markers were measured, and 5 ML models were developed.
Results:
CHI3L1 levels increased progressively with fibrosis severity and discriminated significant fibrosis (≥F2), with AUC 0.84 (95% CI 0.772-0.908) in the overall CHB cohort and 0.839 (95% CI 0.755-0.924) in CHB patients with normal ALT, outperforming conventional biomarkers. Incorporating CHI3L1 into ML further improved diagnostic performance; the Decision Tree model achieved optimal accuracy AUCs of 0.923 [95% CI 0.891-0.976] and 0.895 [95% CI 0.831-0.960], respectively. SHapley Additive exPlanations (SHAP) analysis identified CHI3L1 as the most influential feature (62.2%) in this model. The model performed consistently in the external validation cohort.
Conclusions:
CHI3L1 is a strong noninvasive biomarker for identifying clinically significant fibrosis in CHB. Integration with ML enhances fibrosis staging accuracy, supporting timely antiviral therapy.