Predicting Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: Integration of Two-Dimensional Ultrasound Radiomics with Clinical Features

To evaluate the ability of two-dimensional ultrasound radiomics, integrated with clinical features, to predict central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). We conducted a retrospective study of PTC patients treated at the Second People's Hospital of Changzhou from January 2018 to February 2023. A total of 725 eligible patients were randomly allocated to training and test cohorts in a 7:3 ratio. Radiomic features were extracted from the PTC primary nodal region region on two-dimensional ultrasound images. Dimensionality reduction was performed using Mann-Whitney <i>U</i> tests, Spearman correlation analysis, and least absolute shrinkage and selection operator regression, yielding a radiomics signature (Rad-score). Seven machine-learning algorithms-logistic regression, support vector machine, k-nearest neighbors, decision tree, random forest, light gradient boosting machine, and gaussian naïve bayes-were compared to identify the optimal classifier. A joint predictive model was then constructed by integrating the Rad-score with clinically significant variables identified by univariate and multivariate logistic regression, and implemented using the optimal machine-learning classifier. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Among the seven algorithms, gaussian naïve bayes achieved the highest predictive performance. Univariate and multivariate logistic regression revealed that sex, age, and tumor aspect ratio were independent predictors of CLNM. These variables were integrated with the Rad-score to yield a joint model that achieved AUCs of 0.840 (95% CI, 0.806-0.873) and 0.811 (95% CI, 0.746-0.866) in the training and test cohorts, respectively. Calibration curves and decision curve analysis indicated that the joint model was well-calibrated and afforded favorable clinical utility. The joint model integrating two-dimensional ultrasound radiomics with clinical features enables effective preoperative prediction of CLNM in PTC.