Predictive value of MRI radiomics for postoperative recurrence of liver cancer
10.3969/j.issn.1672-8270.2025.05.012
- VernacularTitle:基于MRI影像组学对肝细胞癌术后复发的预测价值
- Author:
Zhicheng DONG
1
;
Jinbiao ZHANG
;
Mengyang XING
;
Zhibo WANG
;
Geng MENG
;
Junwei MA
Author Information
1. 中国融通医疗健康集团有限公司淄博一四八医院影像中心 淄博 255300
- Publication Type:Journal Article
- Keywords:
Radiomics;
Hepatocellular carcinoma(HCC);
Recurrence;
Magnetic resonance imaging(MRI);
Predictive efficacy
- From:
China Medical Equipment
2025;22(5):57-61
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the clinical application value of a combined model based on the radiomics features of magnetic resonance imaging(MRI)and MRI signs in predicting recurrence after radical resection for hepatocellular carcinoma(HCC).Methods:A retrospective analysis was conducted on the imaging data of 100 patients with radical resection for HCC who admitted to Zibo 148 Hospital from May 2016 to May 2020.All patients underwent abdominal enhanced MRI examination before surgery,and they were followed up for at least 2 years after the surgery.They were randomly divided into training group(70 cases)and verification group(30 cases)as a ratio of 7:3.According to the postoperative follow-up results,the training group existed 12 cases of recurrence and 58 cases without recurrence,and the verification group existed 5 cases of recurrence and 25 cases without recurrence.The 3D-slicer software was used to extract radiomics features of preoperative MRI images of each HCC patient.The intra-group correlation coefficient(ICC)of the extracted imaging features of the observers was calculated.The maximum related minimum redundancy(mRMR)algorithm and LASSO regression were selected to analyze the established radiomics labels after dimensionality reduction and screening.Univariate and multivariate logistic regression analysis were used to screen the independent risk factors of predicting recurrence in MRI signs,and they were used respectively to construct radiomics models with the radiomics labels of plain scan,arterial phase,portal phase and hepatobiliary phase.The receiver operating characteristic(ROC)curve was used to assess the diagnostic efficacy of each radiomics model in predicting recurrence.Results:The ICC range of two physicians in selecting radiomics features from the MRI images of all patients were between 0.903 and 0.957,which consistency was favorable(ICC≥0.9).Compared with other predictive models,the highest area under curve(AUC)values of ROC curve of the radiomics model of plain scan of training group[0.951(95%CI:0.901-1.000)]and verification group[0.968(95%CI:0.917-1.000)]were respectively 0.951 and 0.968 in predicting recurrence after radical resection for liver cancer.Conclusion:The combined model that is constructed on the basis of MRI radiomics features has favorable predictive value for the recurrence of patients after radical resection for HCC.Among of them,the radiomics model of plain scan has a certain guiding role in the clinical implementation of personalized treatment plans under the absence of enhancement,and in underdeveloped areas.
