Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma. Issue 88 (March 2017)

• Record Type: Journal Article
• Authors: Zhao, Hui; Hua, Ye; Dai, Tu; He, Jian; Tang, Min; Fu, Xu; Mao, Liang; Jin, Huihan; Qiu, Yudong
• Is Part Of: European journal of radiology. Issue 88(2017)
• Page Start: 32
• Page End: 40
• Publication Date: 2017-03
• Subjects: HCC hepatocellular carcinoma -- MVI microvascular invasion -- HBsAg hepatitis B surface antigen -- ALT alanine aminotransferase -- TB total bilirubin -- ALB serum albumin -- GGT gamma glutamyl transpeptidase -- AFP alpha-fetoprotein -- PLT platelet count -- CECT contrast-enhanced computed tomography -- SNEG single nodule with extranodular growth -- CMN confluence multinodular -- IG infiltrative growth -- ROC receiver operating characteristic -- PPV positive predictive value -- NPV negative predictive value -- LR likelihood ratios; Microvascular invasion -- Hepatocellular carcinoma -- Predictive scoring model -- Ct; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2016.12.030
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: This study aimed to establish a novel predictive scoring model of MVI in HCC patients. Preoperative imaging features on CECT, such as intratumoral arteries, non-nodule type and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model is of great value in prediction of MVI regardless of tumor size. Abstract: Purpose: Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion. Methods: A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n = 206) and validation cohort (n = 103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts. Results: Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5 cm and >5 cm in AUROC ( P = 0.910). Conclusions: The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI regardless of tumor size.