Related Experiment Video
Updated: Aug 7, 2026
A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography
Published on: July 21, 2023
Lasso Regression-based Model for Cross-Sectional Identification of Significant Hepatic Fibrosis in NAFLD LASSO-based
Wen Deng1, Ya Qin Zhang1, Wei Hua Cao1
1Department of Hepatology Division 2, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China;HBV Infection, Clinical Cure and Immunology Joint Laboratory, Capital Medical University, Beijing 100015, China.
Objective:
Nonalcoholic fatty liver disease (NAFLD) is an increasing global health concern, with liver-related mortality increasing as fibrosis progresses. This study aimed to identify the key determinants and develop a noninvasive model to detect significant hepatic fibrosis.
Methods:
A total of 466 patients with biopsy-confirmed NAFLD were retrospectively analyzed at Beijing Ditan Hospital between 2008 and 2018. The patients were classified into non-significant (S0-1) and significant fibrosis (S2-4) groups. Relevant features were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression to construct a model for the cross-sectional identification of significant fibrosis. Model performance was assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and bootstrap validation.
Results:
Of the 466 patients, 112 had significant fibrosis. LASSO regression identified 10 relevant features, and the model achieved an AUC of 0.919 (sensitivity, 83.9%; specificity, 85.3%) with a corrected AUC of 0.907 after bootstrap validation. It outperformed the APRI, FIB-4, and LSM ( P < 0.001), and the DCA confirmed its clinical utility across probability thresholds.
Conclusion:
The noninvasive model, incorporating demographic, laboratory, and imaging parameters, accurately identified significant hepatic fibrosis in NAFLD and outperformed existing noninvasive scores. This may facilitate interventions and guide personalized management.
