Ultrasound-based intratumoral and peritumoral radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma

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Background Accurate preoperative assessment of lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) remains challenging. We developed and compared ultrasound-based intratumoral, peritumoral, clinical, and combined models for LNM prediction and explored the complementary value of multi-regional imaging. Methods Ninety-nine patients with pathologically confirmed PDAC who underwent preoperative ultrasound were retrospectively enrolled. Intratumoral and 3-mm peritumoral ROIs were manually delineated. Radiomics features were extracted using PyRadiomics and selected via reproducibility filtering, correlation analysis, and LASSO. Nine machine-learning algorithms were evaluated to identify the optimal classifier for each region (intratumoral: logistic regression; peritumoral: random forest). A decision-level combined model was constructed by integrating regional model outputs with clinical information. Discrimination was assessed by AUC with sensitivity, specificity, accuracy, PPV, and NPV; calibration by calibration curves and the Hosmer–Lemeshow test; clinical utility by decision curve analysis (DCA). Model interpretability and inter-model relationships were explored using SHAP, correlation, and Bland–Altman analyses. Results The intratumoral and peritumoral models achieved AUCs of 0.815 and 0.792, respectively. The combined model yielded the highest performance (AUC = 0.898, 95% CI: 0.770–1.000) with good calibration (Hosmer–Lemeshow p = 0.091) and the greatest net benefit on DCA. DeLong tests showed no statistically significant AUC differences among models. Intratumoral and peritumoral outputs were moderately correlated (Pearson’s r = 0.711, p < 0.001), and Bland–Altman analysis demonstrated overall agreement with several outliers, suggesting complementary region-specific information. Conclusion Intratumoral and peritumoral ultrasound radiomics provide complementary information for preoperative LNM prediction in PDAC. The decision-level combined model achieved numerically higher discrimination with favorable calibration, supporting further validation before clinical translation.

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