Ultrasound-based Node-RADS: Introducing a new Scoring System for Ultrasound-based Classification of Lymphadenopathy

Document Type : Original

Authors

1 Department of Radiology, Mashhad University of Medical Sciences, Mashhad, Iran.

2 Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

3 Department of Pathology, Mashhad University of Medical Sciences, Mashhad, Iran.

4 Department of ENT, Mashhad University of Medical Sciences, Mashhad, Iran.

10.22038/ijorl.2025.85674.3883

Abstract

Introduction:
Lymphadenopathy often causes anxiety due to its association with malignancy or serious infections. This study investigates the role of ultrasound features in distinguishing benign from malignant neck lymphadenopathy and proposes a quantitative scoring system (Node-RADS).
Materials and Methods:
This cross-sectional study was conducted at Omid Hospital, Mashhad University of Medical Sciences, Iran. Seven hundred ninety-one patients with neck lymphadenopathy underwent gray-scale and Doppler ultrasound, followed by fine needle aspiration (FNA) or core needle biopsy (CNB) for cytopathological confirmation. Key ultrasound features assessed included Short-Axis Diameter (SAD), Cortical/Hilar Echotexture, and Vascular patterns. A scoring system was developed by assigning malignancy coefficients to each variable. Malignancy coefficients (Wi) were assigned based on the prevalence of malignancy for each feature, and a quantitative Node-RADS score was derived. Diagnostic accuracy was evaluated using ROC analysis. 
Results:
Of 791 patients, 68.5% (542) had malignant lymphadenopathy, predominantly metastases (57.1%, 452). Malignancy coefficients (Wi = 9) were extracted to high-risk features: SAD >16 mm (82% malignancy), Isoechoic cortex with compressed hilum (89%), and non-hilar vascularity (91%). The proposed Node-RADS system achieved an AUC of 0.85 (95% CI: 0.817–0.889), demonstrating strong diagnostic performance.
Conclusion:
The proposed ultrasound-based Node-RADS scoring system correlates significantly with pathologic results, offering an appropriate tool for evaluating cervical superficial lymphadenopathy.
 

Keywords

Main Subjects


  1. Hanzalova I, Matter M. Peripheral lymphadenopathy of unknown origin in adults: a diagnostic approach emphasizing the malignancy hypothesis. Swiss Med Wkly. 2024 Jul. 3; 154(7): 3549. https://doi.org/10.57187/s.3549
  2. Chudobiński C, Świderski B, Antoniuk I, Kurek J. Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale. Cancers 2024; 16(8), 1564; https://doi.org/10. 3390/ cancers 160815 64
  3. Gupta A, Rahman K, Shahid M, Kumar A, Qaseem SD, Hassan SA, et al. Sonographic assessment of cervical lymphadenopathy: Role of high‐resolution and color Doppler imaging. Head & neck. 2011;33(3):297-302.
  4. Ludwig BJ, Wang J, Nadgir RN, Saito N, Castro-Aragon I, Sakai O. Imaging of cervical lymphadenopathy in children and young adults. AJR Am J Roentgenol. 2012 Nov;199(5):1105-13. doi: 10. 2214/AJR.12.8629. PMID: 23096186.
  5. Yang J-R, Song Y, Jia Y-L, Ruan L-T. Application of multimodal ultrasonography for differentiating benign and malignant cervical lymphadenopathy. Japanese Journal of Radiology. 2021;39(10):938-45.
  6. Jaiswal P, Sharma P. Value of ultrasound in evaluation of cervical lymphadenopathy: correlation with FNAC/histopathology. Journal of Society of Surgeons of Nepal. 2016;19(1):13-20.
  7. Burke C, Thomas R, Inglis C, Baldwin A, Ramesar K, Grace R, et al. Ultrasound-guided core biopsy in the diagnosis of lymphoma of the head and neck. A 9 year experience. Br J Radiol. 2011; 84(1004):727-32.
  8. Arian A, Dinas K, Pratilas G C, Alipour S. The Breast Imaging-Reporting and Data System (BI-RADS) Made Easy. IJ Radiol. 2022;19(1):e121155. https://doi.org/10.5812/iranjradiol-121155.
  9. Elsholtz FHJ, Asbach P, Haas M, Becker M, Beets-Tan RGH, Thoeny HC, et al. Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer. Eur Radiol. 2021;31(8):6116-24.
  10. Niu Y, Wen L, Yang Y, Zhang Y, Fu Y, Lu Q, et al. Diagnostic performance of Node Reporting and Data System (Node-RADS) for assessing mesorectal lymph node in rectal cancer by CT. BMC Cancer. 2024; 24(1):716.
  11. Wu Q, Lou J, Liu J, Dong L, Wu Q, Wu Y, et al. Performance of node reporting and data system (node-RADS): a preliminary study in cervical cancer. BMC Medical Imaging. 2024;24(1):28.
  12. Loch FN, Beyer K, Kreis ME, Kamphues C, Rayya W, Schineis C, et al. Diagnostic performance of Node Reporting and Data System (Node-RADS) for regional lymph node staging of gastric cancer by CT. Eur Radiol. 2024;34(5):3183-93.
  13. Chung MS, Choi YJ, Kim SO, Lee YS, Hong JY, Lee JH, et al. A Scoring System for Prediction of Cervical Lymph Node Metastasis in Patients with Head and Neck Squamous Cell Carcinoma. AJNR Am J Neuroradiol. 2019;40(6):1049-54.
  14. Zhong J, Mao S, Chen H, Wang Y, Yin Q, Cen Q, et al. Node-RADS: a systematic review and meta-analysis of diagnostic performance, category-wise malignancy rates, and inter-observer reliability. Eur Radiol. 2024.
  15. Yang X, Yang J, Li J, Leng J, Qiu Y, Ma X. Diagnostic Performance of Node Reporting and Data System Magnetic Resonance Imaging Score in Detecting Metastatic Cervical Lymph Nodes of Nasopharyngeal Carcinoma. Clin Med Insights Oncol. 2024;18:11795549241231564.
  16. Yu P, Wang C, Zhang H, Zheng G, Jia C, Liu Z, et al. Deep learning-based automatic pipeline system for predicting lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma using computed tomography: A multi-center study. Chin J Cancer Res. 2024;36(5):545-61.
  17. Parillo M, Quattrocchi CC. Node Reporting and Data System 1.0 (Node-RADS) for the Assessment of Oncological Patients' Lymph Nodes in Clinical Imaging. J Clin Med. 2025;14(1).
  18. Ryu KH, Lee KH, Ryu J, Baek HJ, Kim SJ, Jung HK, et al. Cervical Lymph Node Imaging Reporting and Data System for Ultrasound of Cervical Lymphadenopathy: A Pilot Study. AJR Am J Roentgenol. 2016;206(6):1286-91.
  19. Alamdaran SA, Randian A, Rasoulian B, Jafarian AH, Aminzadeh B, Niroumand S. Correlation of Sonographic Classification of Neck Adenopathy (A-RADS) and Malignancy. Iran J Otorhinolaryngol. 2023;35(126):39-47.
  20. Mohseni S, Shojaiefard A, Khorgami Z, Alinejad S, Ghorbani A, Ghafouri A. Peripheral Lymphadenopathy: Approach and Diagnostic Tools. Iranian Journal of Medical Sciences. 2014;39(March Supplement):158-70.
  21. Serour, D.K., Mahmoud, B.E., Daragily, B.et al. Lymph nodes in the head and neck cancer: would diffusion-weighted magnetic resonance imaging solve the diagnostic dilemma?. Egypt J Radiol Nucl Med 51, 190 (2020). https://doi.org/10.1186/s43055-020-00311-1
  22. van den Brekel MW, Castelijns JA. What the clinician wants to know: surgical perspective and ultrasound for lymph node imaging of the neck. Cancer Imaging. 2005;5 Spec No A:S41-9. doi: 10.1102/1470-7330.2005.0028.
  23. King AD, Tse GM, Ahuja AT, Yuen EH, Vlantis AC, To EW, et al. Necrosis in metastatic neck nodes: diagnostic accuracy of CT, MR imaging, and US. Radiology. 2004;230:720-6. doi: 10. 1148/ radiol. 2303030157.
  24. Lo CP, Chen CY, Chin SC, Lee KW, Hsueh CJ, Juan CJ, et al. Detection of suspicious malignant cervical lymph nodes of unknown origin: diagnostic accuracy of ultrasound-guided fine-needle aspiration biopsy with nodal size and central necrosis correlate. Can Assoc Radiol J. 2007;58:286-91.
  25. Raja Lakshmi C, Sudhakara Rao M, Ravikiran A, Sathish S, Bhavana SM. Evaluation of reliability of ultrasonographic parameters in differentiating benign and metastatic cervical group of lymph nodes. ISRN Otolaryngol. 2014;2014:238740. doi: 10. 1155/ 2014/238740.