Multimodal radiomics based on dynamic contrast-enhanced ultrasound for predicting cervical lymph node metastasis in papillary thyroid carcinoma with Hashimoto's thyroiditis

This study aimed to develop and validate a multimodal predictive model by integrating radiomic features from B-mode ultrasound (BMUS) and dynamic contrast-enhanced ultrasound (DCE-US) parametric imaging, as well as clinical and conventional ultrasound features, for preoperative prediction of cervical lymph node metastasis (CLNM) in patients with Hashimoto's thyroiditis (HT) complicated with papillary thyroid carcinoma (PTC).

A total of 191 patients with pathologically confirmed HT-complicated PTC were retrospectively enrolled between December 2023 and July 2025. Patients were randomly divided into a training set (n=133) and a validation set (n=58) at a 7:3 ratio using a random number generator. Radiomic features were extracted from BMUS images and DCE-US peak enhancement parametric maps. Feature stability was assessed using intraclass and interclass correlation coefficients (ICC). Key features were selected through a sequential process including the Mann-Whitney U test, recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) regression, followed by calculation of the radiomics score (Rad-score). Independent predictors of CLNM were identified using multivariate logistic regression incorporating clinical and ultrasound features. Three predictive models were constructed: a clinical-ultrasound model, a radiomics model, and a combined multi-modal model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Model interpretability was enhanced using Shapley Additive Explanations (SHAP) analysis.

TG-Ab, TR-Ab, extrathyroidal extension, filling defects, BMUS Rad-score, and DCE-US Rad-score were independent predictors of CLNM (P < 0.05). In the training and validation sets, the combined model achieved area under the curve (AUC) values of 0.936 and 0.916, respectively, which were significantly higher than those of the radiomics model (AUC = 0.825, 0.876) and the clinical-ultrasound model (AUC = 0.760, 0.800; DeLong test, P < 0.05). Calibration curves showed good model fitting. DCA confirmed that the combined model provided greater clinical net benefit. NRI and IDI verified that the combined model significantly improved CLNM prediction performance. SHAP analysis revealed that DCE-US Rad-score was the most important predictor for CLNM.

A multi-modal predictive model combining dual-modality ultrasound radiomic scores (BMUS and DCE-US) with clinical and ultrasound features can effectively and robustly predict CLNM in patients with HT-complicated PTC and performs better than single-modal models. This model provides an objective reference for the formulation of individualized surgical strategies.