Value of texture analysis in evaluating liver cancer recurrence after transarterial chemoembolization

Abstract

Objective: To investigate the feasibility of contrast-enhanced computer tomography (CT) texture analysis in predicting early recurrence after transarterial chemoembolization (TACE) in patients with liver cancer. Methods: A retrospective analysis was performed for 47 patients with liver cancer confirmed by liver biopsy and digital subtraction angiography who underwent upper abdominal contrast-enhanced CT scan before TACE, and according to the presence or absence of focal recurrence within half a year, these patients were divided into early recurrence (ER) group and non-early recurrence (NER) group. The texture analysis was used to delineate tumor boundary layer by layer on the axial contrast-enhanced CT image before liver cancer surgery, and related parameters of tumor heterogeneity, including entropy, mean, non-uniformity, skewness, and kurtosis, were obtained. The independent samples t-test was used for comparison of texture parameters between the two groups. The receiver operating characteristic (ROC) curve was used for the analysis of entropy, mean, and non-uniformity, and the area under the ROC curve (ROC), optical cut-off value, sensitivity, and specificity were calculated to evaluate the efficiency of texture analysis in predicting early focal recurrence after TACE. Results: There were 20 patients in the ER group and 27 in the NER group. The ER group had a maximum major axis length of 88.2±36.3 mm and a maximum minor axis length of 41.4±21.4 mm, and the NER group had a maximum major axis length of 66.9±30.2 mm and a maximum minor axis length of 29.3±19.8 mm; the ER group had significantly higher maximum major and minor axis lengths than the NER group (t = 4.89 and 4.62, P < 0.001). The ER group had significantly higher entropy and non-uniformity values than the NER group, and there were no significant differences in skewness and kurtosis between the two groups. Entropy, non-uniformity, and mean had high efficiency in predicting early recurrence after TACE, and the optimal cut-off value of entropy was 4.135. Conclusion: Volumetric texture analysis of contrast-enhanced CT images before liver cancer surgery has a high value in predicting early recurrence after TACE.