Abstract: Objective: To investigate the application of texture analysis derived from MR imaging in grading of pancreatic neuroendocrine neoplasms(PNENs). Methods: MR imagings of 64 patients with pathologically confirmed PNENs admitted to Ruijin Hospital were enrolled. Texture features were extracted from manually drawn ROIs by using MaZda software, and were selected according to the pathological grade by the feature selection methods. Statistical methods including Fisher coefficient(Fisher), classification error probability combined with average correlation coefficients(POE+ACC), mutual information(MI), and combination of above three methods(FPM) were used to classify the pathological grading of PNENs. The results were shown by misclassification rate. Results: For feature selection methods, FPM had the lowest misclassification rate. Among the statistical methods, the misclassification of NDA was lower than those of RDA, PCA, and LDA. Among the MRI sequences, the ADC map obtained the lowest misclassification rate of 9.38%(6/64), but there was no significant difference between sequences. Conclusions: Texture analysis of MR imaging can be used as an assistant tool for preoperative grading of pancreatic neuroendocrine neoplasms, and when it comes to statistical methods, FPM has the lowest misclassification rate.

Key words: Pancreatic neuroendocrine neoplasms, MRI, Texture analysis, Feature selection, Tumor grade