Radiomics and deep learning models based on unenhanced MRI to predict microvascular invasion in hepatocellular carcinoma:a two-center study
10.3969/j.issn.1002-1671.2025.03.015
- VernacularTitle:基于平扫MRI的影像组学及深度学习模型预测肝细胞癌微血管侵犯:一项双中心研究
- Author:
Ge ZHANG
1
;
Shuyuan ZHONG
;
Genwen HU
;
Xinming LI
;
Xianyue QUAN
Author Information
1. 南方医科大学珠江医院放射科,广东 广州 510280
- Publication Type:Journal Article
- Keywords:
hepatocellular carcinoma;
microvascular invasion;
radiomics;
deep transfer learning;
magnetic resonance imaging
- From:
Journal of Practical Radiology
2025;41(3):424-428
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of radiomics model and deep learning model based on unenhanced MRI in predicting microvascular invasion(MVI)of hepatocellular carcinoma(HCC)preoperatively.Methods A total of 189 patients with postopera-tive pathologically confirmed HCC from two centers were retrospectively selected,of which 119 cases from Zhujiang Hospital of Southern Medical University were used as the training set[60 cases with negative MVI,59 cases with positive MVI],and 70 cases from Shenzhen People's Hospital were used as the external test set[38 cases with negative MVI and 32 cases with positive MVI].Clinical indicators were analyzed by univariate and multivariate logistic regression analysis and the independent predictors of positive MVI were screened.Deep transfer learning(DTL)and traditional radiomics methods were used to construct radiomics model and deep learning model based on unenhanced MRI.The predictive performances of each model were compared using receiver operating charac-teristic(ROC)curves and area under the curve(AUC).DeLong test was employed to compare statistical differences in performance of the models.Results Alkaline phosphatase(ALP)and prothrombin time(PT)were independent predictors of positive MVI(P<0.05).The deep learning model based on T2WI had the best predictive efficacy,with AUC of 0.779[95%confidence interval(CI)0.696-0.863]and 0.741(95%CI 0.620-0.861)in the training set and external test set,respectively,and there were statistically significant differences compared with the radiomics model and the clinical model based on T1WI(P<0.05).Conclusion Deep learning model based on T2WI has a certain application value in preoperative noninvasive prediction of MVI status in HCC patients.
