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Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature
Editorial   Open access

Machine learning fibrosis score for pediatric metabolic dysfunction-associated steatotic liver disease: Promising but premature

Toshifumi Yodoshi
World journal of gastroenterology : WJG, Vol.31(36), p.112217
09/28/2025
DOI: 10.3748/wjg.v31.i36.112217
PMID: 41025074
url
https://doi.org/10.3748/wjg.v31.i36.112217View
Published (Version of record) Open Access

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease in children, affecting up to 38% with obesity of children. With the global shift from non-alcoholic fatty liver disease (NAFLD) to MASLD using affirmative criteria (hepatic steatosis plus ≥ 1 cardiometabolic risk factor) and approximately 99% concordance in pediatrics, the development of non-invasive fibrosis tools is accelerating. Yao et al report a machine-learning “chronic MASLD with fibrosis (CH-MASLD-Fib)” score for advanced fibrosis with area under the receiver operating characteristic curve (AUROC) of 0.92. While timely, we urge caution. First, high accuracy from a single-center cohort signals overfitting: Complex models can learn cohort-specific noise and fail to generalize. Consistent with this, established pediatric scores (NAFLD fibrosis score, fibrosis-4, pediatric NAFLD fibrosis score) perform modestly (AUROC: Approximately 0.6-0.7), and aspartate aminotransferase-to-platelet ratio index is variable, raising concern that CH-MASLD-Fib’s result reflects a statistical artifact. Second, MASLD epidemiology varies by ethnicity (highest in Hispanic, lower in Black children); a model derived in a mono-ethnic Chinese cohort may misclassify other populations. Third, clinical utility and cost-effectiveness are unproven; dependence on specialized assays (e.g., bile acids, cholinesterase) would limit access and increase cost. We recommend external validation in multi-ethnic cohorts, head-to-head comparisons with simple serum indices and elastography, and formal economic analyses. Until then, clinical judgment anchored in readily available markers and judicious, targeted liver biopsy remains paramount.
Health Economics Machine Learning Cost-effectiveness Ethnic diversity External validation Liver fibrosis Metabolic dysfunction-associated steatotic liver disease Non-invasive biomarkers Overfitting

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