Combined Metabolo-Volumetric Parameters of 18F-FDG-PET and MRI Can Predict Tumor Cellularity, Ki67 Level and Expression of HIF 1alpha in Head and Neck Squamous Cell Carcinoma: A Pilot Study

BACKGROUND: Our purpose was to evaluate associations of combined 18F-FDG-PET and MRI parameters with histopathological features in head and neck squamous cell carcinoma (HNSCC). METHODS: Overall, 22 patients with HNSCC were acquired (10 with G1/2 tumors and 12 with G3 tumors).18F-FDG-PET/CT and MRI was performed and maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG) and metabolic tumor volume (MTV) were estimated. Neck MRI was obtained on a 3 T scanner. Diffusion weighted imaging was performed with estimation of apparent diffusion coefficient (ADC). Perfusion parameters Ktrans,Ve, and Kep were derived from dynamic contrast-enhanced (DCE) imaging. Different combined PET/MRI parameters were calculated as ratios: PET parameters divided by ADC or DCE MRI parameters. The following histopathological features were estimated: Ki 67, EGFR, VEGF, p53, hypoxia-inducible factor (HIF)-1α, and cell count. Spearman's correlation coefficient (p) was used for correlation analysis. P < .05 was taken to indicate statistical significance. RESULTS: In overall sample, cellularity correlated with SUVmax/ADCmin (P？=？.558, P？=？.007), TLG/ADCmin (P？=？.546, P？=？.009), and MTV/ADCmin (P？=？.468, P？=？.028). MTV/Kep correlated with expression of HIF-1α (P？=？.450, P？=？0,047). In G1/2 tumors, SUVmax/ADCmin correlated with HIF-1α (P？=？？.648, P？=？.043); MTV/Kep (P？=？？.669, P？=？.034) and TLG/Kep (P？=？？.644, P？=？.044) with Ki67. In G3 tumors, cellularity correlated with SUVmax/ADCmin (P？=？.832, P？=？.001), SUVmax/ADCmean (P？=？.741, P？=？.006), and TLG/ADCmin (P？=？.678, P？=？.015). MTV/ADCmin and TLG/ADCmin tended to correlate with HIF-1α. CONCLUSION: Combined parameters of 18F-FDG-PET and MRI can reflect Ki 67, tumor cellularity and expression of HIF-1α in HNSCC. Associations between parameters of 18F-FDG-PET and MRI and histopathology depend on tumor grading