A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients

Objective To 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). Methods A 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. Results Logistic regression showed the best performance. Platelet count (PLT, OR = 0.991) and shear wave velocity (Vs, OR = 3.563) were independent predictors (P < 0.05). The combined nomogram achieved AUCs of 0.860 (training) and 0.912 (validation), significantly outperforming radiomics-only models, FIB-4, and APRI (P < 0.05). Calibration and decision curves indicated high clinical utility. Conclusion The nomogram integrating 2D-US/SWE radiomics and clinical indexes facilitates non-invasive diagnosis of significant fibrosis in AILDs, thus providing a more reliable quantitative tool for individualized assessments and clinical decision-making. Advances in knowledge This study develops the first nomogram combining multimodal ultrasound radiomics and clinical indexes for noninvasive diagnosis of significant hepatic fibrosis in autoimmune liver diseases, demonstrating superior diagnostic performance.