Limits...
STatistically Assigned Response Criteria in Solid Tumors (STARCIST).

Bengtsson T, Sanabria-Bohorquez SM, McCarthy TJ, Binns DS, Hicks RJ, de Crespigny AJ - Cancer Imaging (2015)

Bottom Line: This finding is consistent with published results, but our data shows greater variability.Application of our method to NSCLC patients treated with erlotinib produces results distinct from those based on the EORTC criteria.Based on data presented here as well as previous repeatability studies, the proposed method has greater statistical power to detect a significant %-decrease on SUVmax compared to published criteria relying on symmetric thresholds.

View Article: PubMed Central - PubMed

Affiliation: Biostatistics, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA. thomasgb@gene.com.

ABSTRACT

Background: Several reproducibility studies have established good test-retest reliability of FDG-PET in various oncology settings. However, these studies are based on relatively short inter-scan periods of 1-3 days while, in contrast, response assessments based on FDG-PET in early phase drug trials are typically made over an interval of 2-3 weeks during the first treatment cycle. With focus on longer, on-treatment scan intervals, we develop a data-driven approach to calculate baseline-specific cutoff values to determine patient-level changes in glucose uptake that are unlikely to be explained by random variability. Our method takes into account the statistical nature of natural fluctuations in SUV as well as potential bias effects.

Methods: To assess variability in SUV over clinically relevant scan intervals for clinical trials, we analyzed baseline and follow-up FDG-PET scans with a median scan interval of 21 days from 53 advanced stage cancer patients enrolled in a Phase 1 trial. The 53 patients received a sub-pharmacologic drug dose and the trial data is treated as a 'test-retest' data set. A simulation-based tool is presented which takes as input baseline lesion SUVmax values, the variance of spurious changes in SUVmax between scans, the desired Type I error rate, and outputs lesion and patient based cut-off values. Bias corrections are included to account for variations in tracer uptake time.

Results: In the training data, changes in SUVmax follow an approximately zero-mean Gaussian distribution with constant variance across levels of the baseline measurements. Because of constant variance, the coefficient of variation is a decreasing function of the magnitude of baseline SUVmax. This finding is consistent with published results, but our data shows greater variability. Application of our method to NSCLC patients treated with erlotinib produces results distinct from those based on the EORTC criteria. Based on data presented here as well as previous repeatability studies, the proposed method has greater statistical power to detect a significant %-decrease on SUVmax compared to published criteria relying on symmetric thresholds.

Conclusions: Defining patient-specific, baseline dependent cut-off values based on the () distribution of naturally occurring fluctuations in glucose uptake enable identification of statistically significant changes in SUVmax. For lower baseline values, the produced cutoff values are notably asymmetric with relatively large changes (e.g. >50 %) required for statistical significance. For use with prospectively defined endpoints, the developed method enables the use of one-armed trials to detect pharmacodynamic drug effects based on FDG-PET. The clinical importance of changes in SUVmax is likely to remain dependent on both tumor biology and the type of treatment.

No MeSH data available.


Related in: MedlinePlus

a) Change in lesion SUVmax in the training dataset plotted vs. the mean of the two measurements. The blue regression line has a slope that is not significantly different from zero. The dashed blue lines are 95 % confidence intervals on the regression line. Appr. 95 % of the changes in SUVmax are within +/− 4 units. b) Relative changes in SUVmax plotted vs. baseline SUVmax for each lesion. The black dashed lines show the ±25 % EORTC cut-off values, while the blue and red dashed lines show the confidence limits based on the Gaussian and t-distributions (5df), respectively
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Fig2: a) Change in lesion SUVmax in the training dataset plotted vs. the mean of the two measurements. The blue regression line has a slope that is not significantly different from zero. The dashed blue lines are 95 % confidence intervals on the regression line. Appr. 95 % of the changes in SUVmax are within +/− 4 units. b) Relative changes in SUVmax plotted vs. baseline SUVmax for each lesion. The black dashed lines show the ±25 % EORTC cut-off values, while the blue and red dashed lines show the confidence limits based on the Gaussian and t-distributions (5df), respectively

Mentions: The histogram plot in Fig. 1b obscures the fact that there is a wide range of baseline SUVmax values among the measured lesions. A Bland-Altman plot, Fig. 2a, shows the same data on SUVmax changes as a function of the mean SUV value for the two timepoints. The main insight from Fig. 2a is that differences in SUVmax between the two measurements are essentially independent of mean SUVmax. Based on the preceeding analyses and plots, we note that the squared differences in SUVmax approximately follow a scaled chi-squared distribution. This distributional information enables us to evaluate the dependence of the variance of the SUVmax differences (i.e. 2σ2) on a set of covariates using a mixed effects Gamma regression. This regression showed no dependence of the variance of the change in SUVmax on trial site, baseline SUVmax, lesion location, diffrence in uptake time, or time between scans (cf., Appendix 1).Fig. 2


STatistically Assigned Response Criteria in Solid Tumors (STARCIST).

Bengtsson T, Sanabria-Bohorquez SM, McCarthy TJ, Binns DS, Hicks RJ, de Crespigny AJ - Cancer Imaging (2015)

a) Change in lesion SUVmax in the training dataset plotted vs. the mean of the two measurements. The blue regression line has a slope that is not significantly different from zero. The dashed blue lines are 95 % confidence intervals on the regression line. Appr. 95 % of the changes in SUVmax are within +/− 4 units. b) Relative changes in SUVmax plotted vs. baseline SUVmax for each lesion. The black dashed lines show the ±25 % EORTC cut-off values, while the blue and red dashed lines show the confidence limits based on the Gaussian and t-distributions (5df), respectively
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4522098&req=5

Fig2: a) Change in lesion SUVmax in the training dataset plotted vs. the mean of the two measurements. The blue regression line has a slope that is not significantly different from zero. The dashed blue lines are 95 % confidence intervals on the regression line. Appr. 95 % of the changes in SUVmax are within +/− 4 units. b) Relative changes in SUVmax plotted vs. baseline SUVmax for each lesion. The black dashed lines show the ±25 % EORTC cut-off values, while the blue and red dashed lines show the confidence limits based on the Gaussian and t-distributions (5df), respectively
Mentions: The histogram plot in Fig. 1b obscures the fact that there is a wide range of baseline SUVmax values among the measured lesions. A Bland-Altman plot, Fig. 2a, shows the same data on SUVmax changes as a function of the mean SUV value for the two timepoints. The main insight from Fig. 2a is that differences in SUVmax between the two measurements are essentially independent of mean SUVmax. Based on the preceeding analyses and plots, we note that the squared differences in SUVmax approximately follow a scaled chi-squared distribution. This distributional information enables us to evaluate the dependence of the variance of the SUVmax differences (i.e. 2σ2) on a set of covariates using a mixed effects Gamma regression. This regression showed no dependence of the variance of the change in SUVmax on trial site, baseline SUVmax, lesion location, diffrence in uptake time, or time between scans (cf., Appendix 1).Fig. 2

Bottom Line: This finding is consistent with published results, but our data shows greater variability.Application of our method to NSCLC patients treated with erlotinib produces results distinct from those based on the EORTC criteria.Based on data presented here as well as previous repeatability studies, the proposed method has greater statistical power to detect a significant %-decrease on SUVmax compared to published criteria relying on symmetric thresholds.

View Article: PubMed Central - PubMed

Affiliation: Biostatistics, Genentech Inc, 1 DNA Way, South San Francisco, CA, 94080, USA. thomasgb@gene.com.

ABSTRACT

Background: Several reproducibility studies have established good test-retest reliability of FDG-PET in various oncology settings. However, these studies are based on relatively short inter-scan periods of 1-3 days while, in contrast, response assessments based on FDG-PET in early phase drug trials are typically made over an interval of 2-3 weeks during the first treatment cycle. With focus on longer, on-treatment scan intervals, we develop a data-driven approach to calculate baseline-specific cutoff values to determine patient-level changes in glucose uptake that are unlikely to be explained by random variability. Our method takes into account the statistical nature of natural fluctuations in SUV as well as potential bias effects.

Methods: To assess variability in SUV over clinically relevant scan intervals for clinical trials, we analyzed baseline and follow-up FDG-PET scans with a median scan interval of 21 days from 53 advanced stage cancer patients enrolled in a Phase 1 trial. The 53 patients received a sub-pharmacologic drug dose and the trial data is treated as a 'test-retest' data set. A simulation-based tool is presented which takes as input baseline lesion SUVmax values, the variance of spurious changes in SUVmax between scans, the desired Type I error rate, and outputs lesion and patient based cut-off values. Bias corrections are included to account for variations in tracer uptake time.

Results: In the training data, changes in SUVmax follow an approximately zero-mean Gaussian distribution with constant variance across levels of the baseline measurements. Because of constant variance, the coefficient of variation is a decreasing function of the magnitude of baseline SUVmax. This finding is consistent with published results, but our data shows greater variability. Application of our method to NSCLC patients treated with erlotinib produces results distinct from those based on the EORTC criteria. Based on data presented here as well as previous repeatability studies, the proposed method has greater statistical power to detect a significant %-decrease on SUVmax compared to published criteria relying on symmetric thresholds.

Conclusions: Defining patient-specific, baseline dependent cut-off values based on the () distribution of naturally occurring fluctuations in glucose uptake enable identification of statistically significant changes in SUVmax. For lower baseline values, the produced cutoff values are notably asymmetric with relatively large changes (e.g. >50 %) required for statistical significance. For use with prospectively defined endpoints, the developed method enables the use of one-armed trials to detect pharmacodynamic drug effects based on FDG-PET. The clinical importance of changes in SUVmax is likely to remain dependent on both tumor biology and the type of treatment.

No MeSH data available.


Related in: MedlinePlus