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Dynamic and Static Approaches to Quantifying ¹⁸F-FDG Uptake for Measuring Cancer Response to Therapy, Including the Effect of Granulocyte CSF

¹ Department of Bioengineering, University of Washington, Seattle, Washington; ² Division of Nuclear Medicine, University of Washington, Seattle, Washington; and ³ Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington

View larger version (100K): [in this window] [in a new window]   FIGURE 1.  Coronal and sagittal 18F-FDG PET images of same patient before treatment (A) and after neoadjuvant chemotherapy including granulocyte CSF (B). Right breast cancer decreased in 18F-FDG activity after treatment (arrowheads), whereas 18F-FDG activity increased in marrow in spine, sternum, and ribs (open arrows).

View larger version (10K): [in this window] [in a new window]   FIGURE 2.  Mean 18F-FDG blood clearance time–activity curves for patients (n = 39) before treatment and after chemotherapy including granulocyte CSF, with close-up of last 4 time bins with SE bars on middle time points (inset). Area under curve significantly decreased after treatment (P = 0.02).

View larger version (10K): [in this window] [in a new window]   FIGURE 3.  SUV vs. MRFDG for all patients before treatment (+ and dashed line) and after granulocyte CSF–containing chemotherapy regimes ( and solid line).

View larger version (9K): [in this window] [in a new window]   FIGURE 4.  Percentage change in SUV vs. percentage change in MRFDG for all (n = 39) patients (A), first patient tertile (, n = 13) with lowest baseline SUVs (B), and second and third patient tertiles (+, n = 26) with higher baseline SUVs (C). Maximum detectable percentage change in SUV (*) is mean percentage alteration in tumor metabolism that can be detected using SUV algorithm assuming that MRFDG model can detect 100% decrease in tumor metabolic activity.