Development and validation of an intra-tumoral and peri-tumoral radiomics model based on dynamic contrast-enhanced ultrasound for predicting lymph node metastasis in type 2 diabetic patients with thyroid cancer

Objectives: This study aimed to develop and validate intra-tumoral and peri-tumoral radiomics models based on dynamic contrast-enhanced ultrasound (CEUS) to preoperatively predict lymph node metastasis (LNM) in thyroid cancer patients with type 2 diabetes. Materials and methods: A total of 203 pathologically confirmed diabetic thyroid cancer patients from three centers were retrospectively included and divided into a training cohort and two external validation cohorts. Radiomics features were extracted from CEUS parameters-time to enhancement (TTE), time to half-peak (TTHP), time to peak (TTP), and washout time (WT). Feature dimensionality reduction was performed using Variance Threshold, SelectKBest, and LASSO regression. Key LNM-related features were screened in the training cohort, and the optimal peri-tumoral region (1 mm vs 2 mm) was determined. Intra-tumoral and peri-tumoral radiomics scores were constructed and integrated into a multivariate logistic regression model. Model performance, calibration, and clinical utility were evaluated across all cohorts. Results: The 2 mm peri-tumoral region yielded a higher AUC than the 1 mm region in all cohorts, with statistical significance in the training cohort and a consistent trend in the external validation cohorts. The combined radiomics model achieved AUCs of 0.930 (95% CI: 0.876-0.964), 0.907 (95% CI: 0.796-0.968), and 0.865 (95% CI: 0.739-0.941) in the training and external validation cohorts. Calibration curves showed good agreement between predicted and actual outcomes, and decision curve analysis demonstrated substantial clinical benefit. Conclusions: The CEUS-based combined radiomics model using intra-tumoral and 2 mm peri-tumoral features provides an effective tool for preoperative LNM prediction in thyroid cancer patients with type 2 diabetes.