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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

Power curves as a function of the treatment effect (δ). Simulation settings S1 and S2 are as shown in Figure 2. In S1 (left),, and in S2 (right),. The solid blue and black lines represent the theoretical power curves for and Ki, respectively (see derivations in Additional file3), while the solid cyan lines show the power improvement. The dotted cyan line shows the peak simulated improvement in power for the two settings S1 and S2 at δ = 0.25 and δ = 0.18, respectively.
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Figure 3: Power curves as a function of the treatment effect (δ). Simulation settings S1 and S2 are as shown in Figure 2. In S1 (left),, and in S2 (right),. The solid blue and black lines represent the theoretical power curves for and Ki, respectively (see derivations in Additional file3), while the solid cyan lines show the power improvement. The dotted cyan line shows the peak simulated improvement in power for the two settings S1 and S2 at δ = 0.25 and δ = 0.18, respectively.

Mentions: Figure 3 shows the theoretical power curves Pk (blue solid line) and Pm (black) for the first and second simulation settings, S1 (left panel) and S2 (right panel). The first case, S1, represents an average study with parameters and σε set at the mean levels and with n = 10; a potential improvement of approximately 10% occurs at a treatment effect of δ = 0.25 (cyan solid line), with a corresponding simulated improvement of 9.8%. The second case, S2, exemplifies a study with a particularly good signal-to-noise ratio, i.e., low σε. Here, an improvement of approximately 29.2% occurs for δ = 0.18, with a simulated improvement of 29.9%.


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)

Power curves as a function of the treatment effect (δ). Simulation settings S1 and S2 are as shown in Figure 2. In S1 (left),, and in S2 (right),. The solid blue and black lines represent the theoretical power curves for and Ki, respectively (see derivations in Additional file3), while the solid cyan lines show the power improvement. The dotted cyan line shows the peak simulated improvement in power for the two settings S1 and S2 at δ = 0.25 and δ = 0.18, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Power curves as a function of the treatment effect (δ). Simulation settings S1 and S2 are as shown in Figure 2. In S1 (left),, and in S2 (right),. The solid blue and black lines represent the theoretical power curves for and Ki, respectively (see derivations in Additional file3), while the solid cyan lines show the power improvement. The dotted cyan line shows the peak simulated improvement in power for the two settings S1 and S2 at δ = 0.25 and δ = 0.18, respectively.
Mentions: Figure 3 shows the theoretical power curves Pk (blue solid line) and Pm (black) for the first and second simulation settings, S1 (left panel) and S2 (right panel). The first case, S1, represents an average study with parameters and σε set at the mean levels and with n = 10; a potential improvement of approximately 10% occurs at a treatment effect of δ = 0.25 (cyan solid line), with a corresponding simulated improvement of 9.8%. The second case, S2, exemplifies a study with a particularly good signal-to-noise ratio, i.e., low σε. Here, an improvement of approximately 29.2% occurs for δ = 0.18, with a simulated improvement of 29.9%.

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