Abstract
OBJECTIVE: To develop and validate a carbon nanoparticle-enhanced nomogram for predicting lateral lymph node (LLN) metastasis in patients with clinically node-negative (cN0) papillary thyroid carcinoma (PTC). METHODS: A retrospective analysis was conducted on 421 cN0 PTC patients treated between 2014 and 2020. Patients were randomly divided into training (n=316) and internal validation (n=105) cohorts. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were performed to identify predictive factors from clinical, ultrasonographic, and carbon nanoparticle tracing data. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). RESULTS: Independent predictors identified included age (HR: 0.944, 95% CI: 0.908-0.982), tumor diameter ≥1 cm (HR: 0.221, 95% CI: 0.053-1.920), regular tumor morphology (HR: 0.090, 95% CI: 0.020-0.470), and the number of carbon nanoparticle-stained positive lateral lymph nodes (HR: 0.000, 95% CI: 0.000-0.231). The nomogram showed excellent discrimination, with an AUC of 0.911 in the training set and 0.916 in the validation set, and good calibration (Brier scores of 5.70 and 4.50, respectively). DCA confirmed the clinical utility of the model across a range of risk thresholds. CONCLUSION: This carbon nanoparticle-guided nomogram is a practical and highly accurate tool for intraoperative risk stratification of LLN metastasis in cN0 PTC patients. Integrating tracer-based lymph node assessment with conventional clinicopathological factors enhances predictive capability compared to existing methods, potentially reducing unnecessary neck dissections while ensuring appropriate management of high-risk cases. Multicenter validation and incorporation of molecular markers are important next steps toward clinical implementation.