Objective To explore the value of expression of carcinomaassociated antigens in early diagnosis and predicting prognosis in gallbladder carcinoma. MethodsThe expression of carcinoembryonic antigen (CEA), carbohydrate antigen (CA50), Ecadherin (ECD) and proliferating cell nuclear antigen (PCNA) in 10 cases of cholecystitis, 10 cases of gallbladder adenomas and 50 cases of gallbladder carcinomas were detected by immunohistochemistry. ResultsThe positive rate of CEA, CA50 and PCNA labeling index (LI) in gallbladder carcinomas were significantly higher than that of gallbladder adenomas and cholecystitis (P＜0.05 and P＜0.01). The positive rate of ECD in gallbladder carcinomas, especially with metastasis, was significantly lower than that of gallbladder adenomas and cholecystitis (P＜0.05). The 3year survival rate was significantly lower in gallbladder carcinomas with CEA and PCNA overexpression (P＜0.05), the 3year survival rate in patients with ECD positive tumors was higher than that of those with negative tumors (P＜0.05). Conclusion The detection of CEA, CA50 and PCNA is useful for early diagnosis of malignant change in gallbladder adenomas and gallbladder carcinomas. Therefore, the CEA, PCNA and ECD might be useful for predicting prognosis of gallbladder carcinomas.
ObjectiveTo establish a model for predicting microvascular invasion (MVI) of hepatocellular carcinoma based on magnetic resonance imaging (MRI) radiomics features.MethodsThe clinical and pathological datas of 190 patients with hepatocellular carcinoma who received surgical treatment in our hospital from September 2017 to May 2020 were prospectively collected. The patients were randomly divided into training group (n=158) and test group (n=32) with a ratio of 5∶1. Gadoxetate disodium (Gd-EOB-DTPA) -enhanced MR images of arterial phase and hepatobiliary phase were used to select radiomics features through the region of interest (ROI). The ROI included the tumor lesions and the area dilating to 2 cm from the margin of the tumor. Based on a machine learning algorithm logistic, a radiomics model for predicting MVI of hepatocellular carcinoma was established in the training group, and the model was evaluated in the test group.ResultsSeven radiomics features were obtained. The area under the receiver operating characteristic curve (AUC) of the training group and the test group were 0.830 [95%CI (0.669, 0.811)] and 0.734 [95%CI (0.600, 0.936)], respectively.ConclusionThe model based on MRI radiomics features seems to be a promising approach for predicting the microvascular invasion of hepatocellular carcinoma, which is of clinical significance for the management of hepatocellular carcinoma treatment.