Computer methods and programs in biomedicine · 2026
Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhu Gangfeng, Yi Qiang, Xu Rui, Xie Yi, Song Yipeng, Wang Xiangcai, Huang Li, Xiang Yi
Аннотация
BACKGROUND: Significant liver fibrosis is often clinically silent but predicts adverse outcomes. We developed and externally validated an interpretable machine-learning (ML) framework using readily obtainable demographic, anthropometric, and clinical variables for population-level pre-screening of significant liver fibrosis.
METHODS: We included 9424 European participants from the UK Biobank, of whom 1678 were classified as high risk using the Fibrosis-4 Index (FIB-4) ≥ 1.45 or NAFLD Fibrosis Score (NFS) ≥ -1.455. Ten ML algorithms were trained and internally evaluated using a stratified training, validation, and test design. XGBoost was further validated in the 2017-2023 National Health and Nutrition Examination Survey (NHANES; n = 15,270) and an independent real-world cohort (n = 694), in which significant fibrosis was defined as liver stiffness measurement ≥ 8.0 kPa. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC), interpretability by Shapley additive explanations and local interpretable model-agnostic explanations, and prognostic relevance by all-cause mortality.
RESULTS: XGBoost achieved AUCs of 0.818 (95% CI, 0.796-0.841) and 0.816 (95% CI, 0.786-0.846) in the internal validation and test cohorts, respectively. In external validation, AUCs were 0.746 (95% CI, 0.735-0.757) in NHANES and 0.793 (95% CI, 0.750-0.836) in the real-world cohort. Interpretation analyses highlighted weight, age, height, hypertension, and waist circumference, with consistent support for anthropometric predictors. Model-defined high-risk status was associated with higher all-cause mortality (log-rank P < 0.001) and remained independently associated with mortality in the internal validation and test cohorts.
CONCLUSION: This XGBoost model, based on readily accessible demographic and clinical variables, shows good potential for accurately identifying individuals at high risk of significant liver fibrosis in primary care settings. This approach serves as a valuable tool for improving early detection strategies and facilitating timely interventions for significant liver fibrosis.
METHODS: We included 9424 European participants from the UK Biobank, of whom 1678 were classified as high risk using the Fibrosis-4 Index (FIB-4) ≥ 1.45 or NAFLD Fibrosis Score (NFS) ≥ -1.455. Ten ML algorithms were trained and internally evaluated using a stratified training, validation, and test design. XGBoost was further validated in the 2017-2023 National Health and Nutrition Examination Survey (NHANES; n = 15,270) and an independent real-world cohort (n = 694), in which significant fibrosis was defined as liver stiffness measurement ≥ 8.0 kPa. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC), interpretability by Shapley additive explanations and local interpretable model-agnostic explanations, and prognostic relevance by all-cause mortality.
RESULTS: XGBoost achieved AUCs of 0.818 (95% CI, 0.796-0.841) and 0.816 (95% CI, 0.786-0.846) in the internal validation and test cohorts, respectively. In external validation, AUCs were 0.746 (95% CI, 0.735-0.757) in NHANES and 0.793 (95% CI, 0.750-0.836) in the real-world cohort. Interpretation analyses highlighted weight, age, height, hypertension, and waist circumference, with consistent support for anthropometric predictors. Model-defined high-risk status was associated with higher all-cause mortality (log-rank P < 0.001) and remained independently associated with mortality in the internal validation and test cohorts.
CONCLUSION: This XGBoost model, based on readily accessible demographic and clinical variables, shows good potential for accurately identifying individuals at high risk of significant liver fibrosis in primary care settings. This approach serves as a valuable tool for improving early detection strategies and facilitating timely interventions for significant liver fibrosis.