Dual-energy CT combined with histogram parameters in the assessment of perineural invasion in colorectal cancer.

Purpose: The purpose is to evaluate the predictive value of dual-energy CT (DECT) combined with histogram parameters and a clinical prediction model for perineural invasion (PNI) in colorectal cancer (CRC).

Methods: We retrospectively analyzed clinical and imaging data from 173 CRC patients who underwent preoperative DECT-enhanced scanning at two centers. Data from Qinghai University Affiliated Hospital (n = 120) were randomly divided into training and validation sets, while data from Lanzhou University Second Hospital (n = 53) served as the external validation set. Regions of interest (ROIs) were delineated to extract spectral and histogram parameters, and multivariate logistic regression identified optimal predictors. Six machine learning models-support vector machine (SVM), decision tree (DT), random forest (RF), logistic regression (LR), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost)-were constructed. Model performance and clinical utility were assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: Four independent predictive factors were identified through multivariate analysis: entropy, CT40_KeV, CEA, and skewness. Among the six classifier models, RF model demonstrated the best performance in the training set (AUC = 0.918, 95% CI: 0.862-0.969). In the validation set, RF outperformed other models (AUC = 0.885, 95% CI: 0.772-0.972). Notably, in the external validation set, the XGBoost model achieved the highest performance (AUC = 0.823, 95% CI: 0.672-0.945).

Conclusion: Dual-energy CT-based combined with histogram parameters and clinical prediction modeling can be effectively used for preoperative noninvasive assessment of perineural invasion in colorectal cancer.