The overarching goal of this study is to develop PET/MR techniques to accurately detect nodal metastases for surgical planning and assessment of treatment response.
Patients (n = 30) with head and neck cancer who are scheduled for node dissection surgery are eligible. Enrolled subjects will undergo one research PET/MR scan at the Center for Biomedical Imaging (660 First Avenue) within one week prior to the planned surgery. During the dissection, the locations of removed lymph nodes will be noted in terms of conventional neck lymph node levels. PET/MR data will be used for development of a combined kinetic model analysis of PET and MRI data (Aim 1). The final PET/MR findings will be compared with pathological evaluation results in order to assess the accuracy of PET/MR for detection of nodal metastases (Aim 2).
Study Type
OBSERVATIONAL
Enrollment
23
Simultaneous PET/MRI (3T system)
NYU Langone Medical Center
New York, New York, United States
Accuracy of simultaneous FDG-PET/MRI for detection of nodal metastases
This study is to assess PET/MR techniques for detection of nodal metastases for surgical planning and assessment of treatment response. Our central hypothesis is glucose metabolic rate (GMR) measured using a simultaneous PET/MR scanner can more accurately detect nodal metastases than standardized uptake value (SUV) measures from PET alone. SUV depends on both tumor metabolic rate and tracer delivery, which makes the interpretation of SUV challenging. For instance, an inflammatory node can have high SUV due to increased vascularity and vascular permeability and cannot be easily differentiated from a metastatic node, based on SUV. However, we hypothesize that inflammatory nodes will have lower GMR than metastatic nodes that contain highly proliferating cancer cells, such that they can be differentiated from metastatic nodes more reliably. This study will also determine if simultaneously acquired MRI can reduce uncertainty in GMR measurement of PET.
Time frame: 2 years after beginning of study (July 2014 - June 2016)
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