The value of intratumoral and peritumoral ultrasound radiomics in predicting lateral cervical lymph node metastasis in papillary thyroid carcinoma: a multicenter study of five hospitals.

Background: Accurate preoperative prediction of lateral cervical lymph node metastasis (LLNM) in papillary thyroid carcinoma (PTC) is crucial for surgical planning but remains challenging with conventional ultrasound. This study aimed to develop and validate a predictive model combining intratumoral and peritumoral ultrasound radiomics with clinical features for assessing LLNM. Methods: Ultrasound images of 328 surgically treated patients with PTC across five medical centers were retrospectively collected and randomly divided into a training set (n=229) and a test set (n=99) at a 7:3 ratio. All patients underwent preoperative ultrasound examinations, and corresponding pathological results were obtained. The region of interest (ROI) within each PTC lesion was defined as the intratumoral region, while a 3 mm extension beyond this ROI was defined as the peritumoral region for radiomics feature extraction. Four clinical variables, including age, nodule length, ultrasound elasticity score, and nodule orientation, were also included in the analysis. Five predictive models were constructed: intratumoral, 3 mm peritumoral, intratumoral combined peritumoral, clinical, and clinical combined 3 mm peritumoral models. The model with the best predictive performance was used to construct a nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves, DeLong tests, and decision curve analysis (DCA). Results: The 3 mm peritumoral model demonstrated superior diagnostic performance compared to the intratumoral and combined intratumoral + 3 mm peritumoral models for predicting LLNM. The areas under the ROC curve (AUCs) for the training set were 0.909 (3 mm peritumoral), 0.891 (intratumoral), and 0.866 (combined intratumoral and 3 mm peritumoral), respectively. The AUCs for the clinical combined 3 mm peritumoral model, 3 mm peritumoral model, and clinical model alone were 0.928, 0.909, and 0.843, respectively (P<0.001). Calibration curves demonstrated good agreement between predicted and observed outcomes. DCA indicated that the model had clinical utility when the threshold probability exceeded 10%. Conclusions: The clinical variables combined with a 3 mm peritumoral model can effectively predict the risk of LLNM in patients with PTC prior to surgery and may provide a reliable imaging-based foundation for personalized surgical planning.