Predicts Value of a Combined Nomogram Model Integrating ultrasound and cytology for ATA risk stratification of lymph node negative papillary thyroid carcinoma

Objectives This study aimed to predict the American Thyroid Association (ATA) risk stratification of lymph node negative (N0) papillary thyroid carcinoma (PTC) patients using preoperative ultrasonic and cytological features to provide guidance for clinical treatment strategies. Methods In this multicenter, retrospective study, 500 N0 PTC patients who underwent total thyroidectomy with lymph node dissection were included across 2 institutions from September 2018 to February 2024. Patients were categorized into low, intermediate-high recurrence risk groups based on the 2009 ATA risk stratification system. Univariate logistic regression analysis was performed to assess the relationship between ultrasonic and cytological features and ATA risk stratification. Significant features ( P  < 0.05) were then incorporated into a multivariate logistic regression model to identify independent predictors of risk stratification. A Nomogram was constructed using predictors from the final multivariate logistic regression. Results Papillary like arrangement,Escape like arrangement, Nucleolus, Size, echo, Margin, and ECE were identified as independent predictors of ATA risk stratification. A Nomogram model was developed based on these predictors, demonstrating good discrimination with a C-index of 0.799. The calibration curve further demonstrated excellent predicted consistency between the predicted metastasis probabilities. Additionally, the Nomogram displayed a C-index of 0.778 in the testing cohort. Conclusions Risk stratification in N0 PTC patients correlates with factors such as Papillary arrangement, Escape like arrangement, Nucleolus, Size, Echo, Margin, and ECE, emphasizing the necessity of closely monitoring patients presenting with these risk factors. Additionally, the Nomogram model integrating seven preoperative risk factors specifically tailored for solitary N0 PTC patients was devised, showcasing notable predictive accuracy for preoperative ATA risk stratification.