A CT Based Radiomics Nomogram For Differentiation Between Focal-type Autoimmune Pancreatitis And Pancreatic Ductal Adenocarcinoma

Objectives: The purpose of this study was to develop and validate an CT-based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma.Methods: 96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study(32 and 64 cases respectively).All cases have been confirmed by imaging,clinical follow-up and/or pathology.Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images.Independent-sample T tests and LASSO regression were used for feature selection.The data is divided into training cohort and test cohort according to the ratio of 7:3.The training cohort was classified using a variety of machine learning-based classifiers,and 5-fold cross-validation has been performed.The classification performance was evaluated using the test cohort.Multivariate logistic regression analysis was then used to develop a radiomics nomogram model,containing the CT findings and Rad-Score.Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model.Different patients have been selected to test and evaluate the model prediction process.Finally,receiver operating characteristic curves and decision curves were plotted,and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.Results: A total of 158 radiomics features were extracted from each image.7 features were selected to construct the radiomics model,then a variety of classifiers were used for classification and multinomial logistic regression(MLR)was selected to be the optimal classifier.Combining CT findings with radiomics model,a prediction model based on CT findings and radiomics was finally obtained.The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts,respectively.The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.Conclusions: The radiomics nomogram model based on CT images can reasonably and accurately distinguish f AIP patients from PDAC patients,which can provide more benefits for clinical disease diagnosis and prognosis.