Construction and validation of a nomogram model for microvascular invasion in hepatocellular carcinoma based on the characteristics on contrast-enhanced ultrasound Liver Imaging Reporting and Data System
10.3969/j.issn.1001-5256.2022.11.016
- VernacularTitle:基于超声造影LI-RADS特征的肝细胞癌微血管侵犯列线图模型的构建及验证
- Author:
Jing XI
1
;
Meiqin GU
1
;
Zuowei BAO
2
Author Information
1. Department of Ultrasound, Wujin Hospital Affiliated to Jiangsu University, Wujin Clinical College, Xuzhou Medical University, Changzhou, Jiangsu 213000, China
2. Department of Ultrasound, Changzhou Third People's Hospital, Changzhou, Jiangsu 213000, China
- Publication Type:Original Articles_Liver Neoplasms
- Keywords:
Carcinoma, Hepatocellular;
Microvascular Invasion;
Contrast-Enhanced Ultrasound;
Nomograms
- From:
Journal of Clinical Hepatology
2022;38(11):2520-2525
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct and validate a nomogram model for predicting microvascular invasion (MVI) based on the characteristics on contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) in patients with hepatocellular carcinoma (HCC). Methods A total of 262 patients with HCC who were diagnosed in Wujin Hospital Affiliated to Jiangsu University from January 2017 to July 2020 were enrolled and randomly divided into modeling group and validation group at a ratio of 1∶ 1, with 131 patients in each group. MVI was confirmed by postoperative microscopic pathological results, and there were 70 patients with MVI in the modeling group and 56 patients with MVI in the validation group. CEUS was used to evaluate LI-RADS characteristics for the two groups. The independent samples t -test was used for comparison of continuous data between the two groups, and the chi-square test was used for comparison of categorical data between the two groups. Univariate and multivariate Logistic regression analyses were used to identify the risk factors for MVI in the modeling group; the receiver operating characteristic (ROC) curve was plotted, and the area under the ROC curve (AUC) was calculated for the model in predicting MVI to evaluate the accuracy of prediction; a decision curve analysis was used to evaluate the consistency of the model, and dispersion was compared between the calibration curve and the standard curve for the model in predicting MVI. Results There were no significant differences in clinical data and CEUS findings between the modeling group and the validation group (all P > 0.05). The univariate analysis showed that compared with the MVI-negative patients, the MVI-positive patients had significant increases in serum alpha fetoprotein (AFP) level, tumor diameter, and LR-5 "late and mild washout" and LR-M "early washout" on LI-RADS, as well as a significantly higher LI-RADS grade (all P < 0.05). The multivariate analysis showed that AFP 20-400 ng/mL (odds ratio [ OR ]=2.65, P < 0.001), AFP≥400 ng/mL ( OR =3.98, P < 0.001), tumor diameter ≥30 mm ( OR =2.12, P < 0.001), and LR-M on CEUS ( OR =3.24, P < 0.001) were independent risk factors for MVI. The ROC curve analysis showed that the nomogram had an AUC of 0.867 and 0.821 in predicting MVI in the modeling group and the validation group, respectively. The nomogram model had a C-Index of 0.765 (95% confidence interval: 0.701-0.834). The calibration curves of the nomogram model were close to the standard curve in both groups. Conclusion The nomogram model based on LI-RADS obtained by CEUS in combination with AFP and tumor diameter has a good application value and can guide the preoperative screening for patients at a high risk of MVI and the development of appropriate surgical plans in clinical practice.
