Ultrasound in medicine & biology · 2026
Ultrasonography-Based Application of Node-RADS in Differentiating Malignant and Benign Superficial Lymph Nodes: A Retrospective Study.
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Zhang Hongjun, Li Renjie, He Danni, Mao Muyi, Zheng Bowen, Yin Tinghui, Wu Tao, Liang Fengping, Ren Jie, Xu Zuofeng
Аннотация
OBJECTIVES: To evaluate the diagnostic accuracy of the Node Reporting and Data System (Node-RADS) adapted for ultrasound (US) in differentiating benign from malignant superficial lymph nodes (LNs).
METHODS: This retrospective study included 505 patients with 564 superficial LNs who underwent US examination and histopathological confirmation between September 2022 and December 2023. Two radiologists independently assessed all target LNs using Node-RADS system based on size and morphologic criteria. Diagnostic performance was evaluated using Receiver Operating Characteristic curve (ROC) analysis, and inter-reader agreement was measured using Cohen's kappa (κ). A multivariable logistic regression model was developed incorporating clinical and sonographic features.
RESULTS: Node-RADS scores were strongly associated with malignancy (Spearman ρ = 0.849, p < 0.001), with good interobserver agreement (κ = 0.782). The optimal diagnostic threshold was Node-RADS >3, yielding an area under the ROC (AUC) of 0.719. The multivariable model incorporating Node-RADS score, cortical thickness, vascular pattern, and malignancy history significantly improved diagnostic accuracy (AUC = 0.903, 95% Confidence Interval: 0.876-0.926).
CONCLUSIONS: Node-RADS adapted for US provides a standardized and reproducible approach for superficial LN evaluation. Integration with clinical and vascular features significantly enhances diagnostic performance, supporting its potential in clinical decision-making.
METHODS: This retrospective study included 505 patients with 564 superficial LNs who underwent US examination and histopathological confirmation between September 2022 and December 2023. Two radiologists independently assessed all target LNs using Node-RADS system based on size and morphologic criteria. Diagnostic performance was evaluated using Receiver Operating Characteristic curve (ROC) analysis, and inter-reader agreement was measured using Cohen's kappa (κ). A multivariable logistic regression model was developed incorporating clinical and sonographic features.
RESULTS: Node-RADS scores were strongly associated with malignancy (Spearman ρ = 0.849, p < 0.001), with good interobserver agreement (κ = 0.782). The optimal diagnostic threshold was Node-RADS >3, yielding an area under the ROC (AUC) of 0.719. The multivariable model incorporating Node-RADS score, cortical thickness, vascular pattern, and malignancy history significantly improved diagnostic accuracy (AUC = 0.903, 95% Confidence Interval: 0.876-0.926).
CONCLUSIONS: Node-RADS adapted for US provides a standardized and reproducible approach for superficial LN evaluation. Integration with clinical and vascular features significantly enhances diagnostic performance, supporting its potential in clinical decision-making.