Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast-Enhanced CT Imaging

The objective of the study is to assess the clinical value of machine learning radiomics based on contrast-enhanced computed tomography (CECT) images in preoperative prediction of perineural invasion (PNI) status in pancreatic ductal adenocarcinoma (PDAC). A total of 143 patients with PDAC were enrolled in this retrospective study (training group, n = 100; test group, n = 43). Radiomics features were extracted from CECT images and selected by the Mann-Whitney U-test, Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO). The logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) algorithms were trained to build radiomics models by radiomic features. Multivariate logistic regression was employed to identify independent predictors and establish clinical models. A combined model was constructed by integrating clinical and radiomics features. Model performances were assessed by receiver operating characteristic curves (ROCs) and decision curve analyses (DCAs). A total of 788 radiomics features were extracted from CECT images, of which 14 were identified as significant through the three-step selection process. Among the machine learning models, the SVM radiomics model exhibited the highest predictive performance in the test group, achieving an area under the curve (AUC) of 0.831, accuracy of 0.698, sensitivity of 0.677, and specificity of 0.750. After logistic regression screening, the clinical model was established using carbohydrate antigen 19-9 (CA199) levels as one independent predictor. In the test group, the clinical model demonstrated an AUC of 0.644, accuracy of 0.744, sensitivity of 0.871, and specificity of 0.417. The combined model showed improved performance compared to both the clinical and radiomics models in the test group, with an AUC of 0.844, accuracy of 0.767, sensitivity of 0.806, and specificity of 0.667. Subsequently, DCA of the combined model indicated optimal clinical value for predicting PNI status. Machine learning radiomics models can accurately predict PNI status in patients with pancreatic ductal adenocarcinoma. The combined model, which incorporates clinical and radiomics features, enhances preoperative diagnostic performance and aids in the selection of treatment methods.