Objective:To investigate the clinical value of radiomics based on computed tomography (CT) examination in preoperative differential diagnosis of pancreatic serous cystadenoma (SCA) and mucinous cystadenoma (MCA).Methods:The retrospective case-control study was conducted. The clinicopathological and imaging data of 154 patients with pancreatic cystic neoplasms who were admitted to the First Affiliated Hospital, Zhejiang University School of Medicine from January 2012 to December 2019 were collected. There were 24 males and 130 females, aged (50±13)years. Of the 154 patients, 99 cases were diagnosed as SCA and 55 cases were diagnosed as MCA. All the 154 patients underwent plain and enhanced CT scan of pancreas before operation. The clinical characteristics, radiology features and radiomics features of all patients were collected to construct the clinical characteristics model, radiology model, radiomics model and fused model. The receiver operating characteristic (ROC) curve of each model was drawn, and those constructed models were evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Based on the optimal model, the nomogram was constructed. Observation indicators: (1) establishment and validation of clinical characteristics model; (2) establishment and validation of radiology model; (3) establishment and validation of radiomics model; (4) establishment and validation of fused model; (5) nomogram of fused model. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Results:(1) Establishment and validation of clinical characteristics model: 3 clinical characteristics, including age, symptoms and preoperative serum CA19-9, were selected using multinomial logistic linear regression analysis to construct the clinical characteristics model. Result of the multinomial logistic linear regression analysis was expressed by formula ①: clinical characteristics model score=0.635-0.007×age+0.054×clinical symptoms+0.108×preoperative serum CA19-9. The ROC curve for the test dataset of clinical characteristics model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of clinical characteristics model were 0.611(95% confidence interval as 0.488?0.734, P<0.05), 56.6%, 66.7%, 56.3%, 41.5%, 78.4% for the training dataset and 0.771(95% confidence interval as 0.624?0.919, P<0.05), 77.8%, 63.1%, 88.5%, 80.1%, 76.7% for the test dataset, respectively. (2) Establishment and validation of radiology model: 5 radiology characteristics, including tumor location, the number of tumors, tumor diameter of cross section, lobulated tumor and polycystic tumor (more than 6), were selected using multinomial logistic linear regression analysis to construct the radiology model. Result of the multinomial logistic linear regression analysis was expressed by formula ②: radiology model score=?0.034+0.300×tumor location+0.202×the number of tumors+0.014×tumor diameter of cross section?0.251×lobulated tumor?0.170×polycystic tumor (more than 6). The ROC curve for the test dataset of radiology model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of radiology model were 0.862(95% confidence interval as 0.791?0.932, P<0.05), 78.8%, 81.8%, 77.5%, 62.8%, 90.2% for the training dataset and 0.853(95% confidence interval as 0.713?0.994), P<0.05), 88.9%, 89.4%, 88.5%, 85.0%, 92.0% for the test dataset, respectively. (3) Establishment and validation of radiomics model: 4 categories of a total 1 067 radiomics features were extracted from 154 patients with pancreatic cystic neoplasms, including 7 first-order histogram features, 53 texture features, 848 wavelet features and 159 local binary pattern features. A total of 896 stable radiomics features were retained to construct the model, based on the condition of intraclass correlation coefficient >0.9. After selected by variance threshold and correlation coefficient threshold, 350 radiomics features were retained. Fifty synthetic radiomics features were constructed based on the original features in order to obtain potential radiomics features, and the total number of radiomics features was 400. After analyzed by the five-fold recursive feature elimination, 22 radiomics features were screened out, including 13 wavelet features, 7 synthetic radiomics features and 2 local binary pattern features. The support vector machine algorithm was used to construct the radiomics model. The penalty coefficient 'C' and parameter 'γ' of the radiomics model were 35.938 and 0.077, respectively. The kernel function of the radiomics model was 'radial basis function kernel'. The ROC curve of radiomics model using 5-fold cross validation was drawn. The average AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the radiomics model were 0.870 ( P<0.05), 83.1%, 81.8%, 83.8%, 73.8% and 89.2%, respectively. (4) Establishment and validation of fused model: the fused model was constructed after selecting the tumor location and lobulated tumor of radiology characteristics and radiomics score. Result of the multinomial logistic linear regression analysis was expressed by formula ③: fused model socre=?0.154+0.218×tumor location?0.223×lobulated tumor+0.621×radiomics score. The ROC curve for the test dataset of fused model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of fused model were 0.893(95% confidence interval as 0.828?0.958, P<0.05), 83.7%, 81.8%, 84.5%, 71.1%, 90.9% for the training dataset and 0.966(95% confidence interval as 0.921?0.999, P<0.05), 91.1%, 84.2%, 96.2%, 94.1%, 89.3% for the test dataset, respectively. (5) Nomogram of fused model: the nomogram of fused model was illustrated with the Youden index of 0.416. Conclusion:The prediction model based on the radiomics signature and radiological features extracted from preoperative CT examination can make the differential diagnosis of pancreatic SCA from MCA.