This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
Objective To summarize the research progress in the diagnosis and treatment of pancreatic cystic neoplasms (PCNs). Method The guidelines and literatures related to the diagnosis and treatment of PCNs were collected and reviewed. Results At present, there was still no clear method to distinguish the types of PCNs and their benign and malignant, and there was still a dispute between domestic and foreign guidelines on the diagnosis and treatment of PCNs. Conclusion Clinical researchers still need to carry out more research, provide higher quality evidence, resolve the disputes existing in different guidelines, standardize the diagnosis and treatment process of PCNs, thus, PCNs can be identified early, diagnosed accurately and intervened in time.