An ultrasound model for predicting recurrence of papillary thyroid carcinoma after complete endoscopic resection

INTRODUCTION: Papillary thyroid cancer (PTC) is one of the most common malignancies involving the endocrine system. AIM: To explore the clinical value of ultrasound-based radiomics for predicting the recurrence of PTC after complete endoscopic resection. MATERIAL AND METHODS: The general data of 361 PTC patients were collected. They were randomly assigned to the modeling group (n = 253) and the validation group (n = 108) according to the ratio of 7 : 3. In the modeling group, the PyRadiomics package was applied to extract radiomic features from preoperative ultrasound images, and least absolute shrinkage and selection operator (LASSO) was used to screen and to construct a radiomics score (Rad-score). Independent prognostic predictors were identified using the Cox proportional hazards model, and a nomogram prediction model was constructed by R software. RESULTS: Using the LASSO regression model, 7 radiomic features were screened and then the Rad-score was calculated. Based on the Rad-score, modeling and validation groups were divided into low-, medium- and high-risk groups, and the 10-year recurrence-free survival rates were 94.7% vs. 95.9%, 83.6% vs. 80.0%, and 50.0% vs. 66.6%, respectively (p < 0.001). Multivariate analysis revealed that age, lymph node metastasis and Rad-score were independent predictors for recurrence-free survival (p < 0.05). CONCLUSIONS: The ultrasound-based radiomics score can effectively predict the postoperative recurrence-free survival in patients with PTC. The nomogram prediction model is superior to the AJCC staging system in terms of predictive accuracy and consistency.