Objective To develop a machine learning model for predicting recurrence risk in papillary thyroid carcinoma (PTC) based on preoperative ultrasound radiomics features combined with key clinicopathological parameters.
Methods A total of 152 patients with PTC who were diagnosed at Guangdong Provincial People’s Hospital between 2017 and 2019, underwent curative surgery, and completed a 5-year postoperative ultrasound follow-up were retrospectively included, comprising 34 patients with recurrence and 118 without recurrence. Preoperative clinicopathological data and ultrasound images were collected, and ultrasound radiomics features were extracted and integrated with key clinicopathological variables. Using stratified random sampling, patients were divided into a training set (n=106) and a testing set (n=46) at a ratio of 7∶3. L2-regularized logistic regression, random forest (RF), support vector machine with a radial basis function kernel (SVM-RBF), and gradient boosting decision tree (GBDT) models were trained. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics, and SHapley Additive exPlanations (SHAP) was applied to interpret the optimal model.
Results The logistic regression model achieved the best performance on the testing set, with an AUC of 0.89, sensitivity of 0.80, and specificity of 0.81. SHAP analysis revealed that key predictors of recurrence included wavelet-L_firstorder_Kurtosis, central lymph node metastasis ratio (CLNR), surgical procedure, BRAF V600E mutation status, and maximum tumor diameter.
Conclusion The logistic regression model demonstrated excellent predictive performance and strong clinical interpretability, suggesting its potential for widespread application in recurrence risk stratification in PTC patients.
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