ArticleAbdominal radiology (New York)2026
A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo develop a prediction model combining radiomics features from 2D ultrasound (2D-US) and shear wave elastography (SWE) with clinical indicators for assessing significant hepatic fibrosis (S2-4) in autoimmune liver diseases (AILDs).
methodsA total of 147 biopsy-confirmed AILD patients were classified into non-significant (S0-1, n = 44) and significant fibrosis (S2-4, n = 103) groups based on Scheuer's classification, and randomly divided into training (n = 102) and validation (n = 45) cohorts. Radiomics features with interclass correlation coefficient > 0.75 were selected. Ten non-zero coefficient features were identified using least absolute shrinkage and selection operator (LASSO) regression. Six machine learning algorithms were evaluated. A nomogram integrating optimal radiomics features and clinical indexes was developed and assessed via ROC, calibration curve, and decision curve analysis.
resultsLogistic regression demonstrated the best performance among all algorithms. Platelet count (PLT, OR = 0.992) and shear wave velocity (Vs, OR = 3.855) were identified as independent predictors for diagnosing S2-4 stage fibrosis (P < 0.05). The combined nomogram achieved AUCs of 0.860 in the training set and 0.912 in the validation set, demonstrating significantly superior diagnostic performance compared to the single radiomics model, FIB-4 index, and APRI index (P < 0.05). In subgroup analyses across various AILD subtypes and different ALT levels, the nomogram model consistently showed the best diagnostic performance.
conclusionThis study combined the radiomics of two-dimensional ultrasound and shear wave elastography and clinical indicators to construct a nomogram model, which can effectively achieve non-invasive diagnosis of AILDs fibrosis and accurately identify significant fibrosis, providing a more reliable quantitative tool for individualized assessment and clinical decision-making.
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