Nomogram of hepatocellular carcinoma ultrasonic feature regression model for predicting microvascular invasion
10.13929/j.issn.1003-3289.2025.10.016
- VernacularTitle:基于肝细胞癌超声表现回归模型列线图预测微血管侵犯
- Author:
Jiahan DONG
1
;
Lin ZHOU
;
Xiaohui WANG
Author Information
1. 郑州大学第一附属医院超声医学科,河南郑州 450052
- Publication Type:Journal Article
- Keywords:
carcinoma,hepatocellular;
ultrasonography;
nomograms;
forecasting;
microvascular invasion
- From:
Chinese Journal of Medical Imaging Technology
2025;41(10):1682-1686
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of nomogram of hepatocellular carcinoma(HCC)ultrasonic feature regression model for predicting microvascular invasion(MVI).Methods A total of 400 HCC patients(432 lesions)confirmed by pathology were retrospectively collected and divided into training set and validation set at the ratio of 7∶3.There were 280 cases(302 lesions)in training set,including 160 cases(172 lesions)of MVI(+)and 120 cases(130 lesions)of MVI(—),while 120 cases(130 lesions)in validation set,including 70 cases(76 lesions)of MVI(+)and 50 cases(54 lesions)of MVI(-).Univariate and multivariate logistic regression analyses were performed to analyze ultrasonic manifestations of HCC.The independent predictors of MVI of HCC were screened out,and a regression model and nomogram were established,and the efficacy,calibration and clinical value of the nomogram for predicting MVI of HCC were evaluated.Results The maximum diameter of HCC(OR=2.564),margin regular or not(OR=0.412),internal echo uniform or not(OR=1.875),the presence or absence of capsule(OR=0.305)and Adler blood flow grade(OR=3.502)were all independent predictors of MVI of HCC(all P＜0.05).The sensitivity,specificity,accuracy and the area under the curve(AUC)of the nomogram for predicting MVI of HCC in training set was 84.88％,81.05％,83.11％and 0.950,respectively,while in validation set was 78.95％,77.78％,78.46％and 0.910,respectively.Calibration curve and decision curve analysis indicated that this nomogram had good calibration and high clinical net benefit in both training and validation sets.Conclusion Nomogram of ultrasound feature regression model could be used to effectively predict MVI of HCC.
