Development and validation of a predictive model for thyroid nodule malignancy risk based on intralesional and perilesional ultrasound radiomic features.

Background: Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) 4a/b thyroid nodules present substantial clinical diagnostic challenges due to wide malignancy risk variation. This study aimed to develop a multimodal integrated predictive model that combines intralesional and perilesional radiomic features with clinical characteristics, and to systematically evaluate its clinical utility in predicting malignancy among thyroid nodules (TNs) classified as C-TIRADS 4a/b. Methods: A retrospective cohort of 147 patients with TNs who underwent thyroid ultrasound and subsequent ultrasound-guided fine-needle aspiration biopsy (US-FNAB) at Jiaxing Maternal and Child Health Care Hospital between August 2023 and September 2025 was enrolled. Participants were randomly allocated in a 7:3 ratio into training (n=102) and validation (n=45) cohorts. ITK-SNAP software was employed to delineate intralesional regions of interest (ROIs), with perilesional ROIs generated through a 2-mm outward expansion. The PyRadiomics module was utilized to extract radiomic features from horizontal intralesional, longitudinal intralesional, and integrated horizontal and longitudinal intra- and perilesional regions. Based on the selected features, three machine learning algorithms-logistic regression (LR), decision tree, and light gradient boosting machine-were implemented to develop radiomics models and clinical-radiomics combined models. Model performance was assessed using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA), with interpretability analysis conducted using the Shapley additive explanations framework. Results: Multivariate LR analysis identified microcalcification, aspect ratio >1, and irregular shape as independent predictors of malignant TNs. The clinical-radiomics combined model demonstrated superior performance in the validation cohort, achieving an area under the curve of 0.866 [95% confidence interval (CI): 0.728-0.970] using the LR algorithm, with an accuracy of 84.4%, sensitivity of 91.3%, and specificity of 77.3%. DCA confirmed that the clinical-radiomics combined model provided the highest net clinical benefit across a wide range of threshold probabilities. SHAP analysis indicated that the combined intralesional-perilesional radiomics scores contributed most substantially to model predictions, highlighting the critical importance of radiomic features. Conclusions: This study successfully developed a multimodal integrated predictive model combining intralesional and perilesional radiomic features with clinical characteristics. The model demonstrated excellent diagnostic performance in differentiating benign from malignant TNs classified as C-TIRADS 4a/b, providing clinicians with a convenient, non-invasive, and highly effective auxiliary diagnostic tool to support clinical decision-making.