Quantitative CT imaging features for hepatocellular carcinoma (HCC) with b-catenin (CTNNB1) gene mutation.

253 Background: To determine whether CT imaging features can provide quantitative biomarkers to differentiate HCC with pathologic B-catenin gene mutation and those without mutation. Methods: Quantitative imaging features were extracted from a database of manually labeled liver with enhancing and non-enhancing tumor tissue,which were established using multiphasic CT images from 17 patients. CT studies were done before each patient underwent surgical removal of the HCC, which were subjected to pathologic analysis to evaluate B-catenin mutation.The mean period between the CT studies and the pathologic analyses was 18 days. According to the pathology results, the patients were divided into two groups: HCC with CTNNB1 mutation and HCC without. Image feature extraction included image gradients, co-occurrence matrix, and pixel neighborhood statistics of the first, second, and third moments. Pairwise analyses of the imaging features were performed on the mutated and non-mutated HCC images and the background liver tissue of both groups. Independent samples t-test and Mann Whitney U test were performed to quantitatively compare between the means of the imaging features extracted from the tumor tissues of both groups and those extracted from the background liver tissue of both groups. Results: Imaging feature analysis of the pairwise difference between the mutated and non-mutated HCC scans for multiple pixel-neighborhood image features are statistically significant.The top stratifying image features include the skewness (p = 0.02), energy (p = .03), and entropy (p = .03) during the venous and arterial phase. Conclusions: This preliminary study demonstrates the feasibility of quantitative imaging feature extraction from CE-CT imaging to differentiate between HCC with proven B-catenin gene mutation and those without mutation. Non-invasive methods of identifying HCC with B-catenin mutations may be clinically beneficial since B-catenin is an important potential target in novel cancer therapies, and identifying B-catenin mutations may also help provide information regarding prognosis.Verifying the quantitative features in larger patient populations is needed to confirm the results of this study.