Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models

Abstract

BACKGROUND

Hepatitis C virus (HCV) poses a formidable global health concern. Non-invasive assessment of hepatic fibrosis has gained increasing importance as liver biopsy, the current gold standard, is less favored as a routine test due to its invasiveness, cost, and risk of complications. Although several clinical scoring systems exist, the potential contribution of lipid biomarkers has not been sufficiently explored.

AIM

To develop and validate a novel diagnostic tool for non-invasive detection of significant hepatic fibrosis in HCV patients using a composite of serum biomarkers, including lipid parameters. It also sought to establish and validate machine-learning (ML) models for predicting significant fibrosis in HCV.

METHODS

A retrospective of 316 chronic HCV patients was analyzed. Candidate predictors of significant hepatic fibrosis were screened by univariate analysis and selected using least absolute shrinkage and selection operator regression, followed by multivariate logistic regression to identify independent predictors. A novel fibrosis risk score (FRS) incorporating platelet count, liver function indices, and lipid biomarkers was developed and compared with three existing clinical scores. Six ML models were additionally developed, and diagnostic performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and F1-score.

RESULTS

The newly developed FRS demonstrated excellent discriminatory performance for significant hepatic fibrosis, with scores ≥ 9 indicating higher risk and achieving an AUC of 0.83 (95% confidence interval: 0.75-0.88) in the training cohort. The model outperformed commonly used non-invasive indices, including aspartate aminotransferase-to-platelet ratio index (AUC = 0.59), fibrosis-4 (AUC = 0.65), and the gamma-glutamyl transpeptidase-to-platelet ratio (AUC = 0.67) at their respective optimal cutoff thresholds of 1.5, 1.45, and 0.4. Among the evaluated ML approaches, the random forest algorithm exhibited the highest diagnostic accuracy, yielding an AUC of 0.92 (95% confidence interval: 0.88-0.95) in the training cohort.

CONCLUSION

The FRS showed robust predictive accuracy for significant hepatic fibrosis in chronic HCV infection and offers a practical, reliable, and user-friendly non-invasive tool. Additionally, ML models substantially enhanced predictive performance. The random forest model demonstrated superior diagnostic potential among ML algorithms, supporting its applicability in fibrosis risk stratification and screening.

Keywords: Chronic hepatitis C; Non-invasive fibrosis assessment; Hepatic fibrosis; Lipid biomarkers; Platelet count; Machine learning; Random forest; AdaBoost

Core Tip: This study developed and validated a simple non-invasive fibrosis risk score for hepatitis C virus using platelet count, lipid markers, and liver function parameters. The score outperformed commonly used clinical tools for detecting significant hepatic fibrosis. Additionally, machine-learning models were evaluated, which outperformed the commonly used clinical risk scoring systems with random forest and AdaBoost demonstrating the highest diagnostic performance for detecting significant hepatic fibrosis in hepatitis C virus infection.