Influencing factors for microvascular invasion in hepatocellular carcinoma and construction of nomogram model based on three-dimensional visualization
10.3760/cma.j.cn115610-20240125-00042
- VernacularTitle:肝细胞癌微血管侵犯的影响因素分析及基于三维可视化列线图模型构建
- Author:
Guanbin LUO
1
;
Chiyu CAI
;
Lianyuan TAO
;
Dongxiao LI
;
Zhuangzhuang YAN
;
Yanbo WANG
;
Liancai WANG
;
Zejun WEN
;
Peigang NING
;
Deyu LI
Author Information
1. 河南大学人民医院肝胆胰腺外科，郑州 450003
- Keywords:
Liver neoplasms;
Microvascular invasion;
Risk factors;
Three-dimensional visualization;
Nomogram model
- From:
Chinese Journal of Digestive Surgery
2024;23(2):280-288
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the influencing factors for microvascular invasion (MVI) in hepatocellular carcinoma based on three-dimensional visualization and the construction of its nomogram model.Methods:The retrospective cohort study method was conducted. The clinico-pathological data of 190 patients with hepatocellular carcinoma who were admitted to Henan University People′s Hospital from May 2018 to May 2021 were collected. There were 148 males and 42 females, aged (58±12)years. The 190 patients were randomly divided into the training set of 133 cases and the validation set of 57 cases by the method of random number table in the ratio of 7:3. The abdominal three-dimensional visualization system was used to characterize the tumor morphology and other imaging features. Observation indicators: (1) analysis of influencing factors for MVI in hepatocellular carcinoma; (2) construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. Measurement data with normal distribution were expressed as Mean± SD, and independent sample t test was used for comparison between groups. Measurement data with skewed distribution were expressed as M( Q1, Q3), and non-parametric rank sum test was used for comparison between groups. Count data were expressed as absolute numbers, and the chi-square test was used for comparison between groups. Corresponding statistical methods were used for univariate analysis. Binary Logistic regression model was used for multivariate analysis. Receiver operator characteristic (ROC) curves were plotted, and the nomogram model was assessed by area under the curve (AUC), calibration curve, and decision curve. Results:(1) Analysis of influencing factors for MVI in hepatocellular carcinoma. Among 190 patients with hepatocellular carcinoma, there were 97 cases of positive MVI (including 63 cases in the training set and 34 cases in the validation set) and 93 cases of negative MVI (including 70 cases in the training set and 23 cases in the validation set). Results of multivariate analysis showed that alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology were independent factors affecting the MVI of patients with hepatocellular carcinoma ( odds ratio=5.06, 3.62, 1.00, 2.02, 2.59, 95% confidence interval as 1.61-15.90, 1.28-10.20, 1.00-1.01, 1.02-3.98, 1.03-6.52, P<0.05). (2) Construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. The results of multivariate analysis were incorporated to construct a nomogram prediction model for MVI of hepatocellular carcinoma. ROC curves showed that the AUC of the training set of nomogram model was 0.85 (95% confidence interval as 0.79-0.92), the optimal fractional cutoff based on the Jordon′s index was 0.51, the sensitivity was 0.71, and the specificity was 0.84. The above indicators of validation set were 0.92 (95% confidence interval as 0.85-0.99), 0.50, 0.90, and 0.82, respectively. The higher total score of the training set suggested a higher risk of MVI in hepatocellular carcinoma. The calibration curves of both training and validation sets of nomogram model fitted well with the standard curves and have a high degree of calibration. The decision curve showed a high net gain of nomogram model. Conclusions:Alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology are independent influencing factors for MVI in patients with hepatocellular carcinoma. A nomogram model constructed based on three-dimensional visualized imaging features can predict MVI in hepatocellular carcinoma.
