Prediction of microvascular invasion based on enhanced mode magnetic resonance imaging for patients with hepatocellular carcinoma
10.3760/cma.j.cn113884-20200418-00207
- VernacularTitle:基于肝细胞癌磁共振成像强化模式的术前微血管侵犯预测研究
- Author:
Wenjie SUN
;
Zhiling GAO
;
Guanhua YANG
;
Yujia GAO
;
Jing JIA
;
Haijing QIU
;
Lin DENG
;
Yong CHEN
- From:
Chinese Journal of Hepatobiliary Surgery
2021;27(3):175-180
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study preoperative MRI imaging and its enhanced mode on tumor features in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).Methods:The clinical data of patients with a solitary HCC who underwent MRI examination followed by surgical resection at the General Hospital of Ningxia Medical University from January 2017 to June 2019 were studied. The patients were divided into the MVI (+ ) and MVI (-) groups according to the findings on postoperative pathological diagnosis. The relationship between the rates of MVI and MRI tumor features including diffusion weighted imaging (DWI) signal, enhancement mode, enhancement type and other imaging characteristics were analysed.Results:Of 84 patients with HCC enrolled into this study, there were 65 males and 19 females. Their age (Mean±SD) was (54.94±11.51) years. MVI (+ ) was found in 46 patients and MVI (-) in 38 patients. The maximum tumor diameters (Mean±SD) of the two groups were (7.08±3.45) cm and (4.28±2.47) cm ( P<0.01). Single-factor analysis and comparison of imaging characteristics of the two groups of patients showed tumor DWI signal, tumor encapsulation, enhancement mode, tumor edge smoothness, abnormal enhancement around tumors, and intratumoral arteries were significantly different ( P<0.05); There were no significant differences in T 1WI signals, T 2WI signals, tumor periphery, and enhancement types between groups. After inputting MVI(+ ) as a risk factor into the logistic regression model, tumor maximum diameters >6.33 cm, type 3/4 enhancement mode, and unsmoothness of tumor edge were independent risk factors (all P<0.05). Through combined diagnosis using ROC curve analysis with a cut-off value of 0.53, the area under the curve was 0.881, the sensitivity 0.870, specificity 0.789, and the Youden index 0.659. Conclusion:The multivariate logistic regression model and combined diagnosis using ROC curve analysis improved the diagnostic efficacy of MVI in its prediction of HCC on imaging studies. The risk predictors were easy to use and to promote in clinical practice.
