The power of FDG-PET to detect treatment effects is increased by glucose correction using a Michaelis constant

BACKGROUND: We recently showed improved between-subject variability in our [(18)F]fluorodeoxyglucose positron emission tomography (FDG-PET) experiments using a Michaelis-Menten transport model to calculate the metabolic tumor glucose uptake rate extrapolated to the hypothetical condition of glucose saturation: [Formula: see text] , where K(i) is the image-derived FDG uptake rate constant, K(M) is the half-saturation Michaelis constant, and [glc] is the blood glucose concentration. Compared to measurements of K(i) alone, or calculations of the scan-time metabolic glucose uptake rate (MR(gluc) = K(i) * [glc]) or the glucose-normalized uptake rate (MR(gluc) = K(i)*[glc]/(100 mg/dL), we suggested that [Formula: see text] could offer increased statistical power in treatment studies; here, we confirm this in theory and practice. METHODS: We compared K(i), MR(gluc) (both with and without glucose normalization), and [Formula: see text] as FDG-PET measures of treatment-induced changes in tumor glucose uptake independent of any systemic changes in blood glucose caused either by natural variation or by side effects of drug action. Data from three xenograft models with independent evidence of altered tumor cell glucose uptake were studied and generalized with statistical simulations and mathematical derivations. To obtain representative simulation parameters, we studied the distributions of K(i) from FDG-PET scans and blood [glucose] values in 66 cohorts of mice (665 individual mice). Treatment effects were simulated by varying [Formula: see text] and back-calculating the mean K(i) under the Michaelis-Menten model with K(M) = 130 mg/dL. This was repeated to represent cases of low, average, and high variability in K(i) (at a given glucose level) observed among the 66 PET cohorts. RESULTS: There was excellent agreement between derivations, simulations, and experiments. Even modestly different (20%) blood glucose levels caused K(i) and especially MR(gluc) to become unreliable through false positive results while [Formula: see text] remained unbiased. The greatest benefit occurred when K(i) measurements (at a given glucose level) had low variability. Even when the power benefit was negligible, the use of [Formula: see text] carried no statistical penalty. Congruent with theory and simulations, [Formula: see text] showed in our experiments an average 21% statistical power improvement with respect to MR(gluc) and 10% with respect to K(i) (approximately 20% savings in sample size). The results were robust in the face of imprecise blood glucose measurements and K(M) values. CONCLUSIONS: When evaluating the direct effects of treatment on tumor tissue with FDG-PET, employing a Michaelis-Menten glucose correction factor gives the most statistically powerful results. The well-known alternative ‘correction’, multiplying K(i) by blood glucose (or normalized blood glucose), appears to be counter-productive in this setting and should be avoided.