Model‑based three‑dimensional texture analysis of contrast‑enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma

• Published in: Oncology letters Vol. 18; no. 1; pp. 720 - 732
• Main Authors: Zhu, Yong‑Jian, Feng, Bing, Wang, Shuang, Wang, Li‑Ming, Wu, Jiang‑Fen, Ma, Xiao‑Hong, Zhao, Xin‑Ming
• Format: Journal Article
• Language: English
• Published: Greece Spandidos Publications 01.07.2019; Spandidos Publications UK Ltd; D.A. Spandidos
• Subjects: Carcinoma; Diagnostic imaging; Hepatitis; Hepatocellular carcinoma; Liver cancer; Medical imaging; Medical prognosis; Mortality; NMR; Nomograms; Nuclear magnetic resonance; Oncology; Patients; Regression analysis; Studies; Taiwan; China; prediction; magnetic resonance imaging; hepatocellular carcinoma; microvascular invasion; texture analysis
• DOI: 10.3892/ol.2019.10378
• ISSN: 1792-1074

The purpose of the present study was to investigate the value of contrast-enhanced magnetic resonance imaging (CE-MRI) texture analysis for preoperatively predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Accordingly, a retrospective study of 142 patients with pathologically confirmed HCC was performed. The patients were divided into two cohorts: The training cohort (n=99) and the validation cohort (n=43), including the MVI-positive group (n=53) and MVI-negative group (n=89). On the basis of three-dimensional texture analysis, 58 features were extracted from the preoperative CE-MR images of arterial-phase (AP) and portal-venous-phase (PP). The t-test or Kruskal-Wallis test, univariate logistic regression analysis and Pearson correlation were applied for feature reduction. Clinical-radiological features were also analyzed. Multivariate logistic regression analysis was used to build the texture model and combined model with clinical-radiological features. The MVI-predictive performance of the models was evaluated using receiver operating characteristic (ROC) analysis and presented using nomogram. Among the clinical features, a significant difference was found in maximum tumor diameter (P=0.002), tumor differentiation (P=0.026) and α-fetoprotein level (P=0.025) between the two groups in the training cohort. Four MR texture features in AP and five in PP images were identified through feature reduction. On ROC analysis, the AP texture model showed better diagnostic performance than did the PP model in the validation cohort, with an area under the curve (AUC) of 0.773 vs. 0.623, sensitivity of 0.750 vs. 0.500, and specificity of 0.815 vs. 0.926. Together with the clinical features, the combined model of AP improved the AUC, sensitivity and specificity to 0.810, 0.811 and 0.790, respectively, which was demonstrated in nomogram. To conclude, model-based texture analysis of CE-MRI could predict MVI in HCC preoperatively and noninvasively, and the AP image shows better predictive efficiency than PP image. The combined model of AP with clinical-radiological features could improve MVI prediction ability.