Standardizing cervical lymph node evaluation in papillary thyroid carcinoma: diagnostic accuracy of Node-RADS on ultrasound
Abstract
Objectives To assess the diagnostic performance of the Node Reporting and Data System (Node-RADS) in ultrasonographic evaluation of cervical lymph node (LN) metastasis in papillary thyroid carcinoma (PTC) and compare it with conventional suspicious sonographic features. Materials and methods This retrospective study included 227 PTC patients (269 LNs) who underwent preoperative cervical LN ultrasound and biopsy (September 2022–December 2023). Two radiologists independently assigned Node-RADS scores (1–5) based on LN size and morphologic features (shape, border, echogenicity). Diagnostic performance was evaluated using ROC analysis, with histopathology as the reference standard. Node-RADS was compared to five American Thyroid Association (ATA)-defined suspicious features (microcalcifications, cystic changes, etc.). Multivariable analysis assessed an integrated model combining Node-RADS with peripheral vascularity. Results Metastatic LNs (63.9%, 172/269) exhibited higher Node-RADS scores (malignancy rates: 35.7% for score 1 vs. 88.2% for score 5). Node-RADS achieved an AUC of 0.720 (95% CI: 0.662–0.773), surpassing individual features (best AUC: 0.703 for peripheral vascularity). Inter-reader agreement was good ( κ = 0.67). A combined Node-RADS and peripheral vascularity model significantly improved diagnostic performance (AUC: 0.825, p < 0.01). Conclusions Node-RADS offers standardized, reproducible risk stratification for cervical LN metastasis in PTC, with moderate diagnostic accuracy. Integration with peripheral vascularity enhances performance, supporting its potential clinical utility in preoperative assessment.
Fields of Study & Subjects
Thyroid Cancer Diagnosis and Treatment
Endometrial and Cervical Cancer Treatments
Thyroid and Parathyroid Surgery
Publication details
- Journal / source
- Frontiers in Endocrinology
- Publisher
- Frontiers Media SA
- Volume
- 17
- Pages
- 1709522–1709522
- Publication date
- 28 January 2026
- Language
- EN