High-Accuracy Nodal Staging of Head and Neck Cancer with USPIO-Enhanced MRI: A New Reading Algorithm Based on Node-to-Node Matched Histopathology

Abstract

Objectives Ultrasmall superparamagnetic iron oxide (USPIO)-enhanced magnetic resonance imaging (MRI) is a potential diagnostic tool for lymph node assessment in patients with head and neck cancer. Validation by radiologic-pathologic correlation is essential before the method is evaluated in clinical studies. In this study, MRI signal intensity patterns of lymph nodes are correlated to their histopathology to develop a new USPIO-enhanced MRI reading algorithm that can be used for nodal assessment in head and neck cancer patients. Materials and Methods Ten head and neck cancer patients underwent in vivo USPIO-enhanced MRI before neck dissection. An ex vivo MRI of the neck dissection specimen was performed for precise coregistration of in vivo MRI with histopathology. Normal clinical histopathological workup was extended with meticulous matching of all lymph nodes regarded as potentially metastatic based on their in vivo MRI signal intensity pattern. On the basis of histopathology of resected nodes, in vivo MRI signal characteristics were defined separating benign from malignant lymph nodes. Results Fifteen of 34 node-to-node correlated lymph nodes with remaining signal intensity on T2∗-weighted MRI were histopathologically metastatic and 19 were benign. Radiological analysis revealed that metastatic lymph nodes showed equal or higher MRI signal intensity when compared with lipid tissue on T2∗-weighted MGRE sequence (15/16 lymph nodes; 94%), whereas healthy lymph nodes showed lower (17/19 lymph nodes; 89%) or complete attenuation of signal intensity (273/279; 98%) when compared with lipid tissue on T2∗-weighted MGRE. Histopathology of all resected specimens identified 392 lymph nodes. Six lymph nodes with (micro)metastases were missed with in vivo MRI. Whether these 6 lymph nodes were correlated to a nonmalignant lymph node on in vivo MRI or could not be detected at all is unclear. Conclusions We developed a new reading algorithm to differentiate benign from malignant lymph nodes in head and neck cancer patients on the basis of their appearance on high-resolution T2∗-weighted USPIO-enhanced MRI. Next steps involve validation of our reading algorithm to further improve the accuracy of neck lymph node staging with USPIO-enhanced MRI in prospective clinical studies with larger number of patients.