Combined alpha-feto protein and contrast-enhanced MRI imaging features in predicting incidence of microvascular invasion in patients with hepatocellular carcinoma
10.3760/cma.j.cn113884-20200602-00299
- VernacularTitle:甲胎蛋白及增强MRI影像特征对肝细胞癌微血管侵犯的预测价值
- Author:
Wencui LI
;
Lizhu HAN
;
Juxiang MA
;
Zhaoxiang YE
- From:
Chinese Journal of Hepatobiliary Surgery
2021;27(4):266-269
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study the predictive value of combining alpha-feto protein (AFP) with contrast-enhanced MRI imaging features in predicting incidence of microvascular invasion (MVI) in patients with hepatocellular carcinoma.Methods:The data of 206 patients with hepatocellular carcinoma treated at Tianjin Medical University Cancer Institute and Hospital from January 2017 to April 2019 were retrospectively analyzed. There were 179 males and 27 females, with an average age of 58.7 years. The roles of preoperative MRI imaging features and clinical data on predicting the incidence of MVI in patients with hepatocellular carcinoma were evaluated by univariate and multivariate logistic regression analyses. Multivariable regression analysis was then used to plot a nomogram.Results:There were 86 patients (41.7%) with MVI positivity and 120 patients (58.3%) with MVI negativity. Multivariate logistic regression analysis showed that AFP >400 μg/L ( OR=3.318, 95% CI: 1.243-8.855, P=0.017), two-trait predictor of venous invasion (TTPVI) ( OR=13.111, 95% CI: 6.797-28.119, P<0.001), diffusion weighted imaging/T 2 weighted imaging (DWI/T 2WI) mismatch ( OR=17.233, 95% CI: 4.731-44.490, P<0.001), and rim enhancement( OR=5.665, 95% CI: 2.579-18.152, P=0.013) predicted increased risks of MVI in patients with hepatocellular carcinoma. The constructed nomogram directly predicted the risk of MVI in these patients. Conclusions:AFP>400 μg/L, TTPVI, DWI/T 2WI mismatch and rim enhancement were independent risk factors in predicting MVI in patients with hepatocellular carcinoma. This predictive model of MVI which was based on multivariate logistic regression analysis was helpful to clinicians in making individualized treatment plans for patients with hepatocellular carcinoma.
