Accuracy of MRI-based Node-RADS in predicting metastatic lymph node involvement in Head and Neck Squamous Cell Carcinoma.

OBJECTIVES: This study aimed to assess the accuracy of the Node-RADS scoring system in assessing metastatic lymph node (LN) involvement in patients with head and neck squamous cell carcinoma (HNSCC), a critical factor for treatment planning and prognosis.

MATERIALS AND METHODS: A retrospective analysis was conducted on 42 HNSCC patients who underwent preoperative MRI and lymph node dissection. Two radiologists independently evaluated the MRI scans using the Node-RADS system, and the results were compared with postoperative histopathological findings. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy of Node-RADS were calculated, considering LNs with Node-RADS scores of 4 and 5 as positive. LN features such as size, texture, necrosis, border irregularity, and shape were analyzed for their correlation with metastatic involvement. Irregular/ill-defined borders were also evaluated for the detection of Extranodal extension (ENE+).

RESULTS: Out of 118 LNs assessed, 26 (22.03 %) were metastatic (N+), and 8 (7.78 %) were ENE+. Node-RADS demonstrated a sensitivity of 61.54 %, specificity of 89.69 %, PPV of 76.19 %, NPV of 89.69 %, and an overall accuracy of 87.29 %. Significant correlations were observed between LN characteristics like necrosis, border irregularity, shape, and histopathological results (p < 0.0001).

CONCLUSION: The Node-RADS system exhibited high specificity and accuracy in identifying metastatic LNs in HNSCC patients, making it a promising tool for standardizing preoperative imaging assessments. However, further studies are needed to validate its application and improve its integration with advanced imaging techniques for enhanced diagnostic precision.