Statistical analysis
lymphadenopathy
REGRESSION, PANCREATIC ATROPHY, LYMPHADENOPATHY
Continuous variables are exhibited as means and ranges and were compared using the Independent-Samples T-Test. Categorical variables were exhibited as frequency and proportion and compared using the chi-square or Fisher’s exact probability tests. To determine the optimal cutoff value of the width of MPD and the size of mural nodules, Youden index was calculated every variation of 0.5 mm. Univariate analysis and multivariate binary logistic regression models were used to explore the factors that may help distinguish the benign and malignant IPMN. We used simple random sampling provided by SPSS version 25.0 to choose 70% of our patients as an exploratory group to build up a predictive model for malignant IPMN. We draw the ROC curve and calculated the sensitivity and specificity. Then we used the remaining 30% data to validate the efficacy of our model. Data of the validation group was substituted into the logistic model to test our predictive model. To further improve the prediction significance of our model, we used random forest method in our analysis. Age, gender, smoking history, CA19-9, size of cysts, width of MPD, size of mural nodules, pancreatic atrophy, and lymphadenopathy were included in the model. Like the logistic regression model, we randomly chose 70% and 30% of the patients into the exploratory and validation group. Mean decrease accuracy and mean decrease Gini were calculated and dot chart was plotted to exhibit the importance rank of different variables. All reported P values were 2-sided with a value of 0.05. All the statistical analyses in our study were performed using SPSS version 25.0 and RStudio version 1.2.5033 for Windows.