The value of nomogram for predicting microvascular invasion based on clinical and Gd-EOB-DTPA-enhanced magnetic resonance imaging features.
10.3760/cma.j.cn112152-20211101-00803
- VernacularTitle:基于临床和Gd－EOB－DTPA增强磁共振特征构建中国肝癌临床分期Ⅰa期肝细胞癌微血管侵犯的列线图预测模型
- Author:
Qian Qian GUO
1
;
Xiao Hong MA
1
;
Rong Cheng HAN
2
;
Xin Ming ZHAO
1
Author Information
1. Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
2. Department of Diagnostic Radiology, Guizhou Provincial People's Hospital, Guiyang 550002, China.
- Publication Type:Journal Article
- Keywords:
Carcinoma, Hepatocellular;
Magnetic resonance imaging;
Microvascular invasion;
Nomogram
- From:
Chinese Journal of Oncology
2023;45(8):666-672
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the risk factors of microvascular invasion (MVI) in China liver cancer staging system stage Ⅰa (CNLC Ⅰa) hepatocellular carcinoma (HCC), and develop a nomogram for predicting MVI based on clinical and radiographic data. Methods: This retrospective study focused on CNLC Ⅰa HCC patients who underwent radical resection at the Cancer Hospital, Chinese Academy of Medical Sciences from January 2016 to December 2020. Patients' clinical characteristics and laboratory test results and pre-surgery gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging results were collected. The clinical and radiographic risk factors for MVI were identified by univariate and multivariate logistic regression analyses and used for the construction of the predictive nomogram. The nomogram model was then internally validated, and its performance was assessed. Results: A total of 104 patients were divided into the MVI-positive group (n=28) and the MVI-negative group (n=76). Multivariate logistic regression analysis at the P<0.1 level identified serum alpha-ferroprotein >7 ng/ml, total bilirubin >21 μmol/L, prothrombin time >12.5 s, non-smooth margin, and incomplete or absent capsule as risk factors of MVI, based on which a nomogram model was built. The model achieved an area under the curve (AUC) value of 0.867 (95% confidence interval, 0.791-0.944) in the internal validation. The sensitivity and specificity of the nomogram model were 0.786 and 0.829, respectively, with the prediction curve nearly overlapping the ideal curve. Based on the Hosmer-Lemeshow test, the predicted and real results were not significantly different (P=0.956). Conclusions: The probability of MVI of CNLC Ⅰa HCC can be objectively predicted by the monogram model that quantifies the clinical and radiographic risk factors. The model can also help clinicians select individualized surgical plans to improve the long-term prognosis of patients.
