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

Williams SP, Flores-Mercado JE, Baudy AR, Port RE, Bengtsson T - EJNMMI Res (2012)

Bottom Line: The greatest benefit occurred when Ki measurements (at a given glucose level) had low variability.Even when the power benefit was negligible, the use of MRglucmax carried no statistical penalty.The results were robust in the face of imprecise blood glucose measurements and KM values.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Imaging, Genentech, Inc,, 1 DNA Way, South San Francisco, CA, 94080, USA. williams.simon@gene.com.

ABSTRACT

Background: We recently showed improved between-subject variability in our [18F]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: MRglucmax=Ki*(KM+[glc]), where Ki is the image-derived FDG uptake rate constant, KM is the half-saturation Michaelis constant, and [glc] is the blood glucose concentration. Compared to measurements of Ki alone, or calculations of the scan-time metabolic glucose uptake rate (MRgluc = Ki * [glc]) or the glucose-normalized uptake rate (MRgluc = Ki*[glc]/(100 mg/dL), we suggested that MRglucmax could offer increased statistical power in treatment studies; here, we confirm this in theory and practice.

Methods: We compared Ki, MRgluc (both with and without glucose normalization), and MRglucmax 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 Ki from FDG-PET scans and blood [glucose] values in 66 cohorts of mice (665 individual mice). Treatment effects were simulated by varying MRglucmax and back-calculating the mean Ki under the Michaelis-Menten model with KM = 130 mg/dL. This was repeated to represent cases of low, average, and high variability in Ki (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 Ki and especially MRgluc to become unreliable through false positive results while MRglucmax remained unbiased. The greatest benefit occurred when Ki measurements (at a given glucose level) had low variability. Even when the power benefit was negligible, the use of MRglucmax carried no statistical penalty. Congruent with theory and simulations, MRglucmax showed in our experiments an average 21% statistical power improvement with respect to MRgluc and 10% with respect to Ki (approximately 20% savings in sample size). The results were robust in the face of imprecise blood glucose measurements and KM 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 Ki by blood glucose (or normalized blood glucose), appears to be counter-productive in this setting and should be avoided.

No MeSH data available.


Related in: MedlinePlus

Experimental statistical power at day 7 post-dose. Three panels correspond to three animal models from Table 2. Each shows Student's t-test results from treatment comparisons of control and treatment groups of mice as a function of sample size and using three PET metrics. (A) HCT116 colorectal cancer in Nu/Nu mice. (B) A2058 melanoma cancer in Nu/Nu mice; (C) A375 melanoma cancer in Nu/Nu mice. Results were calculated for the full group size of n animals and for all possible combinations of individuals (limited to a maximum of 4,000 random samples) studied in four progressively smaller subsets (x-axis). The y-axis (log10 scale) indicates the significance level p-value. The purple dashed line indicates a significance level of 0.05. Every boxplot includes a bold horizontal line that indicates the median p-value. The box length shows the interquartile range (25% to 75%), and the whiskers show minimum and maximum observed p-values.
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Figure 1: Experimental statistical power at day 7 post-dose. Three panels correspond to three animal models from Table 2. Each shows Student's t-test results from treatment comparisons of control and treatment groups of mice as a function of sample size and using three PET metrics. (A) HCT116 colorectal cancer in Nu/Nu mice. (B) A2058 melanoma cancer in Nu/Nu mice; (C) A375 melanoma cancer in Nu/Nu mice. Results were calculated for the full group size of n animals and for all possible combinations of individuals (limited to a maximum of 4,000 random samples) studied in four progressively smaller subsets (x-axis). The y-axis (log10 scale) indicates the significance level p-value. The purple dashed line indicates a significance level of 0.05. Every boxplot includes a bold horizontal line that indicates the median p-value. The box length shows the interquartile range (25% to 75%), and the whiskers show minimum and maximum observed p-values.

Mentions: A preliminary analysis confirmed that our A375 (n = 9), A2058 (n = 9), and HCT116 (n = 12 per group) tumor studies were powered with sufficient numbers of animals to detect large treatment effect sizes using any FDG-PET metric: Ki, MRgluc, or. To examine how studies with less power might perform, we undertook the simulations described below and supplemented those with a meta-analysis of smaller groups obtained by sampling within our experimental data. We considered the full cohort of animals prepared for a given study to be the ‘universe’ of animals from which the smaller groups were drawn randomly using sampling without replacement. We calculated results (presented in Figure 1) for every possible combination of individuals as long as the number of combinations totaled less than 4,000; when more combinations were possible, we randomly sampled 4,000 cases to generate our results.


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

Williams SP, Flores-Mercado JE, Baudy AR, Port RE, Bengtsson T - EJNMMI Res (2012)

Experimental statistical power at day 7 post-dose. Three panels correspond to three animal models from Table 2. Each shows Student's t-test results from treatment comparisons of control and treatment groups of mice as a function of sample size and using three PET metrics. (A) HCT116 colorectal cancer in Nu/Nu mice. (B) A2058 melanoma cancer in Nu/Nu mice; (C) A375 melanoma cancer in Nu/Nu mice. Results were calculated for the full group size of n animals and for all possible combinations of individuals (limited to a maximum of 4,000 random samples) studied in four progressively smaller subsets (x-axis). The y-axis (log10 scale) indicates the significance level p-value. The purple dashed line indicates a significance level of 0.05. Every boxplot includes a bold horizontal line that indicates the median p-value. The box length shows the interquartile range (25% to 75%), and the whiskers show minimum and maximum observed p-values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472222&req=5

Figure 1: Experimental statistical power at day 7 post-dose. Three panels correspond to three animal models from Table 2. Each shows Student's t-test results from treatment comparisons of control and treatment groups of mice as a function of sample size and using three PET metrics. (A) HCT116 colorectal cancer in Nu/Nu mice. (B) A2058 melanoma cancer in Nu/Nu mice; (C) A375 melanoma cancer in Nu/Nu mice. Results were calculated for the full group size of n animals and for all possible combinations of individuals (limited to a maximum of 4,000 random samples) studied in four progressively smaller subsets (x-axis). The y-axis (log10 scale) indicates the significance level p-value. The purple dashed line indicates a significance level of 0.05. Every boxplot includes a bold horizontal line that indicates the median p-value. The box length shows the interquartile range (25% to 75%), and the whiskers show minimum and maximum observed p-values.
Mentions: A preliminary analysis confirmed that our A375 (n = 9), A2058 (n = 9), and HCT116 (n = 12 per group) tumor studies were powered with sufficient numbers of animals to detect large treatment effect sizes using any FDG-PET metric: Ki, MRgluc, or. To examine how studies with less power might perform, we undertook the simulations described below and supplemented those with a meta-analysis of smaller groups obtained by sampling within our experimental data. We considered the full cohort of animals prepared for a given study to be the ‘universe’ of animals from which the smaller groups were drawn randomly using sampling without replacement. We calculated results (presented in Figure 1) for every possible combination of individuals as long as the number of combinations totaled less than 4,000; when more combinations were possible, we randomly sampled 4,000 cases to generate our results.

Bottom Line: The greatest benefit occurred when Ki measurements (at a given glucose level) had low variability.Even when the power benefit was negligible, the use of MRglucmax carried no statistical penalty.The results were robust in the face of imprecise blood glucose measurements and KM values.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Imaging, Genentech, Inc,, 1 DNA Way, South San Francisco, CA, 94080, USA. williams.simon@gene.com.

ABSTRACT

Background: We recently showed improved between-subject variability in our [18F]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: MRglucmax=Ki*(KM+[glc]), where Ki is the image-derived FDG uptake rate constant, KM is the half-saturation Michaelis constant, and [glc] is the blood glucose concentration. Compared to measurements of Ki alone, or calculations of the scan-time metabolic glucose uptake rate (MRgluc = Ki * [glc]) or the glucose-normalized uptake rate (MRgluc = Ki*[glc]/(100 mg/dL), we suggested that MRglucmax could offer increased statistical power in treatment studies; here, we confirm this in theory and practice.

Methods: We compared Ki, MRgluc (both with and without glucose normalization), and MRglucmax 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 Ki from FDG-PET scans and blood [glucose] values in 66 cohorts of mice (665 individual mice). Treatment effects were simulated by varying MRglucmax and back-calculating the mean Ki under the Michaelis-Menten model with KM = 130 mg/dL. This was repeated to represent cases of low, average, and high variability in Ki (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 Ki and especially MRgluc to become unreliable through false positive results while MRglucmax remained unbiased. The greatest benefit occurred when Ki measurements (at a given glucose level) had low variability. Even when the power benefit was negligible, the use of MRglucmax carried no statistical penalty. Congruent with theory and simulations, MRglucmax showed in our experiments an average 21% statistical power improvement with respect to MRgluc and 10% with respect to Ki (approximately 20% savings in sample size). The results were robust in the face of imprecise blood glucose measurements and KM 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 Ki by blood glucose (or normalized blood glucose), appears to be counter-productive in this setting and should be avoided.

No MeSH data available.


Related in: MedlinePlus