The Feasibility of Combining ADC Value With Texture Analysis of T2WI, DWI and CE-T1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma - Jiang, Xueyan; Jia, Haodong; Zhang, Zhongyuan; Wei, Chao; Wang, Chuanbin; Dong, Jiangning (Frontiers in Oncology, 2022)
Purpose: To evaluate the feasibility of apparent diffusion coefficient (ADC) value combined with texture analysis (TA) in preoperatively predicting the expression levels of Ki-67 and p53 in endometrial carcinoma (EC) patients. Methods: Clinical, pathological and MRI findings of 110 EC patients were analyzed retrospectively. The expression levels of Ki-67 and p53 in EC tissues was detected by immunohistochemistry. ADC value was calculated, and three-dimensional (3D) texture features were measured on T2-weighted images (T2WI), diffusion-weighted images (DWI), and contrast-enhanced T1-weighted images (CE-T1WI). The univariate and multivariate logistic regression and cross-validations were used for the selection of texture features. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of prediction model by the area under the curve (AUC) in the training and validation cohorts. Results: Significant differences of the ADC values were found in predicting Ki-67 and p53 (P=0.039, P=0.007). The AUC of the ADC value in predicting the expression levels of Ki-67 and p53 were 0.698, 0.853 and 0.626, 0.702 in the training and validation cohorts. The AUC of the TA model based on T2WI, DWI, CE-T1WI, and ADC value combined with T2WI + DWI + CE-T1WI in the training and validation cohorts for predicting the expression of Ki-67 were 0.741, 0.765, 0.733, 0.922 and 0.688, 0.691, 0.651, 0.938, respectively, and for predicting the expression of p53 were 0.763, 0.805, 0.781, 0.901 and 0.796, 0.713, 0.657, 0.922, respectively. Conclusion: ADC values combined with TA are beneficial for predicting the expression levels of Ki-67 and p53 in EC patients before surgery, and they provide higher auxiliary diagnostic values for clinical application.