Related Experiment Video
Updated: Jul 7, 2025
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Machine-learning model comprising five clinical indices and liver stiffness measurement can accurately identify
Rong Fan1, Ning Yu1, Guanlin Li2,3
1Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangdong Provincial Clinical Research Center for Viral Hepatitis, Key Laboratory of Infectious Diseases Research in South China, Ministry of Education, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Background & Aims:
aMAP score, as a hepatocellular carcinoma risk score, is proven to be associated with the degree of chronic hepatitis B-related liver fibrosis. We aimed to evaluate the ability of aMAP score for metabolic dysfunction-associated steatotic liver disease (MASLD; formerly NAFLD)-related fibrosis diagnosis and establish a machine-learning (ML) model to improve the diagnostic performance.
Methods:
A total of 946 biopsy-proved MASLD patients from China and the United States were included in the analysis. The aMAP score, demographic/clinical indices and liver stiffness measurement (LSM) were included in seven ML algorithms to build fibrosis diagnostic models in the training set (N = 703). The performance of ML models was evaluated in the external validation set (N = 125).
Results:
The AUROCs of aMAP versus fibrosis-4 index (FIB-4) and aspartate aminotransferase-platelet ratio (APRI) in cirrhosis and advanced fibrosis were (0.850 vs. 0.857 [P = 0.734], 0.735 [P = 0.001]) and (0.759 vs. 0.795 [P = 0.027], 0.709 [P = 0.049]). When using dual cut-off values, aMAP had a smaller uncertainty area and higher accuracy (26.9%, 86.6%) than FIB-4 (37.3%, 85.0%) and APRI (59.0%, 77.3%) in cirrhosis diagnosis. The seven ML models performed satisfactorily in most cases. In the validation set, the ML model comprising LSM and 5 indices (including age, sex, platelets, albumin and total bilirubin used in aMAP calculator), built by logistic regression algorithm (called LSM-plus model), exhibited excellent performance. In cirrhosis and advanced fibrosis detection, the LSM-plus model had higher accuracy (96.8%, 91.2%) than LSM alone (86.4%, 67.2%) and Agile score (76.0%, 83.2%), respectively. Additionally, the LSM-plus model also displayed high specificity (cirrhosis: 98.3%; advanced fibrosis: 92.6%) with satisfactory AUROC (0.932, 0.875, respectively) and sensitivity (88.9%, 82.4%, respectively).
Conclusions:
The aMAP score is capable of diagnosing MASLD-related fibrosis. The LSM-plus model could accurately identify MASLD-related cirrhosis and advanced fibrosis.
Insights
The aMAP score effectively diagnoses metabolic dysfunction-associated steatotic liver disease (MASLD)-related fibrosis. A machine learning model, LSM-plus, accurately identifies MASLD-related cirrhosis and advanced fibrosis.
Area of Science:
- Hepatology
- Medical Diagnostics
- Machine Learning in Medicine
Background:
- The aMAP score is recognized for its association with liver fibrosis in chronic hepatitis B.
- Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing concern with significant fibrosis implications.
- Accurate diagnosis of MASLD-related fibrosis is crucial for patient management.
Purpose of the Study:
- To evaluate the aMAP score's efficacy in diagnosing MASLD-related fibrosis.
- To develop and validate a machine learning (ML) model to enhance MASLD fibrosis diagnosis.
- To compare the diagnostic performance of the aMAP score and ML models against existing methods.
Main Methods:
- Analysis of 946 biopsy-proven MASLD patients from China and the US.
- Inclusion of aMAP score, clinical indices, and liver stiffness measurement (LSM) in seven ML algorithms.
- External validation of developed ML models on a separate cohort.
Main Results:
- The aMAP score demonstrated diagnostic capability for MASLD-related fibrosis, with comparable performance to FIB-4 and APRI in certain aspects.
- Machine learning models showed satisfactory performance, with the LSM-plus model exhibiting excellent diagnostic accuracy for cirrhosis and advanced fibrosis (96.8% and 91.2%, respectively) in validation.
- The LSM-plus model achieved high specificity (98.3% for cirrhosis, 92.6% for advanced fibrosis) and satisfactory AUROCs (0.932 and 0.875).
Conclusions:
- The aMAP score is a viable tool for assessing MASLD-related fibrosis.
- The developed LSM-plus ML model significantly improves the accuracy of identifying MASLD-related cirrhosis and advanced fibrosis.
- This study highlights the potential of integrating clinical data with ML for improved non-invasive liver disease diagnosis.
