Abdominal radiology (New York) · 2025
A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.
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Wang Zixian, Yu Qiying, Sun Yanan, Liang Yanlou, Chen Shanshan, Xie Shuhui, Wu Jing
Аннотация
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 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.
CONCLUSION: This 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.
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 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.
CONCLUSION: This 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.