Pancreatic Serous Cystadenomas And Mucinous Cystadenomas:Differential Diagnosis By Imaging Features And Enhanced CT Texture Analysis

Objective A diagnostic model was established by combining imaging features with enhanced CT portal phase texture analysis to differentiate between pancreatic serous cystadenoma(SCN)and pancreatic mucinous cystadenoma(MCN).Materials and methods Fifty-seven patients with SCN and 43 patients with MCN confirmed by surgery or biopsy from January 2010 to October 2019 were retrospectively analyzed.Demographic and radiological information,such as age,main complaint,size,location,central scar,calcification,thickening of cyst wall and other situation were collected.Total 271 texture features like histogram features、the gray-level co-occurrence matrix features etc.were extracted through Ma Zda.The lesions were randomly divided into training and validation group with a ratio of 7:3.As for training group,least absolute shrinkage and selection operator and classification and regression trees algorithm were used to extract features.Validation group was used for verifying.Univariate analyses and binary logistic regression analyses including clinical,image features and texture features,wereperformed to identify independent factors and establish a diagnostic model.Comparison between the logistic regression model based on the combination of image features and texture features and the logistic regression model based on the combination of image features or texture features.Receiver operating characteristic curves were used,the prediction efficiency of the three models was quantified by area under the curve(AUC),and the sensitivity and specificity of each model were recorded.Results The average age of SCN patients was 52.3 ± 13.0 years old,and 75.4% of them were female;while the average age of MCN patients was 45.6 ± 13.9 years old,and 90.7% of them were female,the difference between them was statistically significant(P < 0.05).There was significant difference in the location,diameter,distribution of calcification and thickening of the cystic wall between the two groups(P<0.05).However,there was no significant difference in symptoms,tumor marker and tumor morphology(P > 0.05).Twenty-three texture features were extracted by the minimum λ value = 0.0106,which was determined through the 10 fold cross validation method in lasso algorithm.After remove irrelevant statistical variables,finally,10 texture features were included in cart decision tree model.According to the minimum "xerror" value = 0.4667,CP value =0.0667,the decision tree model was established,and finally one texture feature(s(4,4)sumentrp)was selected.The accuracy of the training group was 80%,AUC was 0.804(95% CI: 0.692-0.889),sensitivity was 77.5%,specificity was 83.33%;and the accuracy of the validation group was 63.3%,AUC was 0.658(95% CI: 0.464-0.821),sensitivity was 47.06%,specificity was 84.62%.Multivariate logistic regression analysis of image combined with texture features showed that cyst wall < 3 mm(OR: 21.52,95% CI: 1.21-383.3)and S(4,4)sumentrp >1.243(OR: 8.08,95% CI: 2.78-23.47)were the key factors for diagnosis of SCN(P <0.05).Multivariate logistic regression analysis of image features showed that location ofthe pancreatic head and neck(OR: 3.96,95% CI: 1.25-12.5),diameter ≤ 51.2 mm(OR:2.9,95% CI: 1.13-7.46)and cyst wall thickness < 3 mm(OR: 12.61,95% CI:1.03-153.8)were the diagnostic factors of SCN(P < 0.05).As for model combined imaging features with texture features,the AUC of the logistic regression model was0.779(95% CI: 0.685-0.856),the sensitivity was 68.42%,and the specificity was83.72%.As for model contained imaging features only,the AUC of the logistic regression model was 0.762(95% CI: 0.666-0.841),the sensitivity was 84.21%,and the specificity was 58.14%.As for model included texture feature only,the AUC of the logistic regression model was 0.758(95% CI: 0.662-0.838),the sensitivity was 70.18%,and the specificity was 81.4%.Conclusion This study showed that texture features can distinguish SCN from MCN of pancreas to some extent,and the imaging features can also distinguish them.Furthermore,the combination of imaging features and texture features is more effective in distinguishing SCN from MCN than that of imaging features or texture features alone.