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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

Estimates ofand standard deviation (ε) in the 66 studies described in Table2. Illustrative cases discussed in the text are marked as S1, S2, and S3.
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Figure 2: Estimates ofand standard deviation (ε) in the 66 studies described in Table2. Illustrative cases discussed in the text are marked as S1, S2, and S3.

Mentions: To get representative simulations, we chose parameter values based on output from fitting the MM model to FDG-PET data from each of the 66 (as-yet-untreated) experimental cohorts of mice described in Table 2. For these studies, with the half-rate Michaelis constant set at KM = 130 mg/dL[14], the scatter plot in Figure 2 shows estimates of versus σε. For, the sample mean and standard deviation were 47.9 and 12.7, respectively (range = 31.0 to 92.0), and for σε, they were 0.048 and 0.018, respectively (range = 0.022 to 0.113). Based on these values, the first simulation setting (‘S1’, noted on the face of Figure 2) represents an ‘average’ case with and σε set at their sample mean values of 48 and 0.048. The second (‘S2’) and third (‘S3’) settings (likewise noted on the face of Figure 2) represent cases with strong and weak signal-to-noise ratios, where and σε are set to (55, 0.028) and (38, 0.057), respectively. In each simulation, glucose was sampled according to [glc] ~ N(90, 252), the approximate marginal distribution of glucose across the sample data, and KM remained fixed at 130 mg/dL.


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)

Estimates ofand standard deviation (ε) in the 66 studies described in Table2. Illustrative cases discussed in the text are marked as S1, S2, and S3.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Estimates ofand standard deviation (ε) in the 66 studies described in Table2. Illustrative cases discussed in the text are marked as S1, S2, and S3.
Mentions: To get representative simulations, we chose parameter values based on output from fitting the MM model to FDG-PET data from each of the 66 (as-yet-untreated) experimental cohorts of mice described in Table 2. For these studies, with the half-rate Michaelis constant set at KM = 130 mg/dL[14], the scatter plot in Figure 2 shows estimates of versus σε. For, the sample mean and standard deviation were 47.9 and 12.7, respectively (range = 31.0 to 92.0), and for σε, they were 0.048 and 0.018, respectively (range = 0.022 to 0.113). Based on these values, the first simulation setting (‘S1’, noted on the face of Figure 2) represents an ‘average’ case with and σε set at their sample mean values of 48 and 0.048. The second (‘S2’) and third (‘S3’) settings (likewise noted on the face of Figure 2) represent cases with strong and weak signal-to-noise ratios, where and σε are set to (55, 0.028) and (38, 0.057), respectively. In each simulation, glucose was sampled according to [glc] ~ N(90, 252), the approximate marginal distribution of glucose across the sample data, and KM remained fixed at 130 mg/dL.

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