The value of texture analysis based on enhanced CT imaging in predicting risk classification of Gastrointestinal Stromal Tumor

MENG jianmin; CHEN Liguang

Abstract：Objective To investigate the feasibility of predicting the risk of gastrointestinal stromal tumors (GISTs) with CT enhanced texture analysis. Methods 55 cases with gastrointestinal stromal tumors confirmed by surgical pathology were included in our study. Parameters related to tumor heterogeneity obased on Histogram texture analysis and grayscale co-occurrence matrix texture analysis were extracted, including mean, Variance, skewness, kurtosis kurtosis, 10％ pixel values, 50％ pixel values, 90％ pixel values, energy, correlation, contrast and entropy. According to postoperative pathological results, GISTs were divided into very low/low risk group, medium risk group, and high risk group. The differences in texture parameters and the correlation between texture parameters and tumor grade, diameter and Ki-67 were analyzed. Diagnostic performance of texture parameters were analyzed for predicting very low/low risk group and medium-high risk group with ROC curve. Results There was no significant difference in mean, skewness, variance, 10％ pixel values, 50％ pixel values, 90％ pixel values, contrast and autocorrelation between different risk GISTs (all P>0.05). There were significant differences in kurtosis, energy and entropy between the three groups (all P<0.05). The kurtosis and entropy were positively correlated with tumor diameter, Ki-67 index and tumor risk grade (all P<0.05). There was a negative correlation between energy and tumor diameter, Ki-67 index and tumor risk grade (all P<0.05). The results of ROC curve analysis showed that the sensitivity was 71.8％ and the specificity was 63.7％ when the cut-off value of kurtosis was 0.63; the sensitivity was 79.4％ and the specificity was 70.1％ when the cut-off value of energy was 0.20; The sensitivity was 85.3％ and the specificity was 76.8％ when the cut-off value of entropy was 1.10. Conclusion Texture analysis based on enhanced CT may play an important role in assessing preoperative risk classification of GISTs, of which energy and entropy parameters are the most valuable.