Noninvasive Imaging of Cycling Hypoxia in Head and Neck Cancer Using Intrinsic Susceptibility MRI.
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Purpose: To evaluate intrinsic susceptibility (IS) MRI for the identification of cycling hypoxia, and the assessment of its extent and spatial distribution, in head and neck squamous cell carcinoma (HNSCC) xenografts and patients.Experimental Design: Quantitation of the transverse relaxation rate, R2*, which is sensitive to paramagnetic deoxyhemoglobin, using serial IS-MRI acquisitions, was used to monitor temporal oscillations in levels of paramagnetic deoxyhemoglobin in human CALR xenografts and patients with HNSCC at 3T. Autocovariance and power spectrum analysis of variations in R2* was performed for each imaged voxel, to assess statistical significance and frequencies of cycling changes in tumor blood oxygenation. Pathologic correlates with tumor perfusion (Hoechst 33342), hypoxia (pimonidazole), and vascular density (CD31) were sought in the xenografts, and dynamic contrast-enhanced (DCE) MRI was used to assess patient tumor vascularization. The prevalence of fluctuations within patient tumors, DCE parameters, and treatment outcome were reported.Results: Spontaneous R2* fluctuations with a median periodicity of 15 minutes were detected in both xenografts and patient tumors. Spatially, these fluctuations were predominantly associated with regions of heterogeneous perfusion and hypoxia in the CALR xenografts. In patients, R2* fluctuations spatially correlated with regions of lymph nodes with low Ktrans values, typically in the vicinity of necrotic cores.Conclusions: IS-MRI can be used to monitor variations in levels of paramagnetic deoxyhemoglobin, associated with cycling hypoxia. The presence of such fluctuations may be linked with impaired tumor vasculature, the presence of which may impact treatment outcome. Clin Cancer Res; 23(15); 4233-41. ©2017 AACR.
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Carcinoma, Squamous Cell
Cell Line, Tumor
Head and Neck Neoplasms
Magnetic Resonance Imaging
Squamous Cell Carcinoma of Head and Neck
Xenograft Model Antitumor Assays
Radiotherapy Physics Modelling
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Clin Cancer Res, 2017, 23 (15), pp. 4233 - 4241