Journal of clinical gastroenterology · 2025
Development and Validation of a Machine Learning-based Model for Prediction of Liver Fibrosis and MASH.
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Liu Maojie, Jiang Longfeng, Yang Juan, Yao Yao, Puyang Xuerong, Ge Xinyuan, Lu Jing, Zhang Lu, Yan Yuqian, Shen Hongbing, Song Ci
Аннотация
BACKGROUND AND AIM: The development of accurate noninvasive tests to identify individuals with metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis is of great clinical importance. In this study, we aimed to develop 2 noninvasive diagnostic models on the basis of routine clinical and laboratory data, using machine learning, to identify patients with MASH and significant fibrosis (fibrosis stages 2 to 4), respectively.
METHODS: This analysis included the training (n=456) and the validation (n=105) sets of patients who underwent liver biopsy and laboratory testing for liver disease at 2 hospitals in China. Logistic regression, random forest, support vector machine, and the XGBoost algorithm were used to construct models, respectively. The best diagnostic models for MASH and significant fibrosis were compared with 7 existing noninvasive scoring systems including AAR, AST to platelet ratio index (APRI), BARD score, fibrosis-4 (FIB-4), fibrotic non-alcoholic steatohepatitis (NASH) index (FNI), homeostatic model assessment of insulin resistance (HOMA-IR), and non-alcoholic fatty liver disease fibrosis score (NFS). Performance was estimated by the area under the receiver operating characteristic curve (AUROC).
RESULTS: The final noninvasive diagnostic model integrated 19 indicators derived from routine clinical and laboratory tests. The XGBoost models exhibited superior performance in MASH and significant fibrosis with an improved AUROC value (MASH, 0.670, 95% CI 0.530-0.811; significant fibrosis, 0.713, 95% CI 0.611-0.815) compared with other noninvasive scoring systems in the validation set.
CONCLUSIONS: Utilizing machine learning can assist in diagnosing MASH and significant fibrosis based on clinical epidemiological information with good diagnostic performance.
METHODS: This analysis included the training (n=456) and the validation (n=105) sets of patients who underwent liver biopsy and laboratory testing for liver disease at 2 hospitals in China. Logistic regression, random forest, support vector machine, and the XGBoost algorithm were used to construct models, respectively. The best diagnostic models for MASH and significant fibrosis were compared with 7 existing noninvasive scoring systems including AAR, AST to platelet ratio index (APRI), BARD score, fibrosis-4 (FIB-4), fibrotic non-alcoholic steatohepatitis (NASH) index (FNI), homeostatic model assessment of insulin resistance (HOMA-IR), and non-alcoholic fatty liver disease fibrosis score (NFS). Performance was estimated by the area under the receiver operating characteristic curve (AUROC).
RESULTS: The final noninvasive diagnostic model integrated 19 indicators derived from routine clinical and laboratory tests. The XGBoost models exhibited superior performance in MASH and significant fibrosis with an improved AUROC value (MASH, 0.670, 95% CI 0.530-0.811; significant fibrosis, 0.713, 95% CI 0.611-0.815) compared with other noninvasive scoring systems in the validation set.
CONCLUSIONS: Utilizing machine learning can assist in diagnosing MASH and significant fibrosis based on clinical epidemiological information with good diagnostic performance.