Frontiers in physiology · 2026
Integrated model based on ultrasound attenuation and metabolic biomarkers for noninvasive assessment of hepatic fat fraction categories in MASLD: a QCT-referenced study.
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Ma Zejun, Wang Hongbin, Sun Xiaojie, Zhou Shuyu, Sun Ye, Wang Xiaoguang, Li Tao, Yang Xiaohe, Xu Jiancheng, Guo Wanjun, Wang Meng, Kow Alfred Wei Chieh, Zhang Huimao, Zhang Lei, Sun Xiaofeng
Аннотация
BACKGROUND: Hepatic steatosis is a common metabolic disorder for which accessible noninvasive assessment remains clinically relevant. This study aimed to evaluate the QCT-referenced performance of ultrasound attenuation (USAT) and an integrated model combining metabolic biomarkers for non-invasive categorization according to hepatic fat fraction in metabolic dysfunction-associated steatotic liver disease (MASLD), using quantitative computed tomography (QCT)-derived hepatic fat fraction categories as pragmatic imaging comparator labels.
METHODS: A total of 172 participants were enrolled and categorized into hepatic fat fraction categories by QCT. USAT values, along with serum levels of ALT, AST, and ferritin, were collected. Three models were evaluated: USAT-only, laboratory-only, and an integrated USAT + laboratory model. Model performance was assessed using fold-separated internal validation, including nested five-fold stratified cross-validation with feature selection performed within training folds. Harrell's optimism-corrected bootstrap analysis was also performed as a supplementary internal validation method.
RESULTS: USAT values increased significantly with hepatic fat fraction categories (P<0.001). In fold-separated internal validation, the USAT-only model achieved an AUC of 0.847 (95% CI, 0.780-0.902) for QCT-referenced detection of imaging-defined steatosis, while the laboratory-only model achieved an AUC of 0.753 (95% CI, 0.665-0.829). A fixed integrated model including USAT, ALT, and ferritin achieved an AUC of 0.845 (95% CI, 0.772-0.906), but did not significantly improve AUC compared with USAT alone. Multiclass categorization remained exploratory and limited, particularly for Category 2, which included only 31 participants and showed weak discrimination in the corrected random forest analysis (AUC=0.569 for the USAT + ALT + ferritin model). Subgroup analyses showed higher performance in females, younger participants, and those with higher BMI.
CONCLUSION: USAT shows promise as a noninvasive adjunct for QCT-referenced detection of imaging-defined hepatic steatosis in a single-center Chinese health-examination cohort, pending external validation. Adding ALT and ferritin may improve sensitivity and calibration, but did not significantly improve AUC over USAT alone. Because discrimination for the intermediate Category 2 group remained weak, the current model should not be considered reliable for full hepatic fat fraction categorization. Further validation in external, multicenter, and more diverse populations is required before any clinical use.
METHODS: A total of 172 participants were enrolled and categorized into hepatic fat fraction categories by QCT. USAT values, along with serum levels of ALT, AST, and ferritin, were collected. Three models were evaluated: USAT-only, laboratory-only, and an integrated USAT + laboratory model. Model performance was assessed using fold-separated internal validation, including nested five-fold stratified cross-validation with feature selection performed within training folds. Harrell's optimism-corrected bootstrap analysis was also performed as a supplementary internal validation method.
RESULTS: USAT values increased significantly with hepatic fat fraction categories (P<0.001). In fold-separated internal validation, the USAT-only model achieved an AUC of 0.847 (95% CI, 0.780-0.902) for QCT-referenced detection of imaging-defined steatosis, while the laboratory-only model achieved an AUC of 0.753 (95% CI, 0.665-0.829). A fixed integrated model including USAT, ALT, and ferritin achieved an AUC of 0.845 (95% CI, 0.772-0.906), but did not significantly improve AUC compared with USAT alone. Multiclass categorization remained exploratory and limited, particularly for Category 2, which included only 31 participants and showed weak discrimination in the corrected random forest analysis (AUC=0.569 for the USAT + ALT + ferritin model). Subgroup analyses showed higher performance in females, younger participants, and those with higher BMI.
CONCLUSION: USAT shows promise as a noninvasive adjunct for QCT-referenced detection of imaging-defined hepatic steatosis in a single-center Chinese health-examination cohort, pending external validation. Adding ALT and ferritin may improve sensitivity and calibration, but did not significantly improve AUC over USAT alone. Because discrimination for the intermediate Category 2 group remained weak, the current model should not be considered reliable for full hepatic fat fraction categorization. Further validation in external, multicenter, and more diverse populations is required before any clinical use.