Brenda F Kurland1, Mark Muzi2, Lanell M Peterson2, Robert K Doot3, Kristen A Wangerin2, David A Mankoff3, Hannah M Linden4, Paul E Kinahan2. 1. Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania bfk10@pitt.edu. 2. Department of Radiology, University of Washington, Seattle, Washington. 3. Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and. 4. Division of Medical Oncology, University of Washington, Seattle, Washington.
Abstract
UNLABELLED: Uptake time (interval between tracer injection and image acquisition) affects the SUV measured for tumors in (18)F-FDG PET images. With dissimilar uptake times, changes in tumor SUVs will be under- or overestimated. This study examined the influence of uptake time on tumor response assessment using a virtual clinical trials approach. METHODS: Tumor kinetic parameters were estimated from dynamic (18)F-FDG PET scans of breast cancer patients and used to simulate time-activity curves for 45-120 min after injection. Five-minute uptake time frames followed 4 scenarios: the first was a standardized static uptake time (the SUV from 60 to 65 min was selected for all scans), the second was uptake times sampled from an academic PET facility with strict adherence to standardization protocols, the third was a distribution similar to scenario 2 but with greater deviation from standards, and the fourth was a mixture of hurried scans (45- to 65-min start of image acquisition) and frequent delays (58- to 115-min uptake time). The proportion of out-of-range scans (<50 or >70 min, or >15-min difference between paired scans) was 0%, 20%, 44%, and 64% for scenarios 1, 2, 3, and 4, respectively. A published SUV correction based on local linearity of uptake-time dependence was applied in a separate analysis. Influence of uptake-time variation was assessed as sensitivity for detecting response (probability of observing a change of ≥30% decrease in (18)F-FDG PET SUV given a true decrease of 40%) and specificity (probability of observing an absolute change of <30% given no true change). RESULTS: Sensitivity was 96% for scenario 1, and ranged from 73% for scenario 4 (95% confidence interval, 70%-76%) to 92% (90%-93%) for scenario 2. Specificity for all scenarios was at least 91%. Single-arm phase II trials required an 8%-115% greater sample size for scenarios 2-4 than for scenario 1. If uptake time is known, SUV correction methods may raise sensitivity to 87%-95% and reduce the sample size increase to less than 27%. CONCLUSION: Uptake-time deviations from standardized protocols occur frequently, potentially decreasing the performance of (18)F-FDG PET response biomarkers. Correcting SUV for uptake time improves sensitivity, but algorithm refinement is needed. Stricter uptake-time control and effective correction algorithms could improve power and decrease costs for clinical trials using (18)F-FDG PET endpoints.
UNLABELLED: Uptake time (interval between tracer injection and image acquisition) affects the SUV measured for tumors in (18)F-FDG PET images. With dissimilar uptake times, changes in tumor SUVs will be under- or overestimated. This study examined the influence of uptake time on tumor response assessment using a virtual clinical trials approach. METHODS:Tumor kinetic parameters were estimated from dynamic (18)F-FDG PET scans of breast cancerpatients and used to simulate time-activity curves for 45-120 min after injection. Five-minute uptake time frames followed 4 scenarios: the first was a standardized static uptake time (the SUV from 60 to 65 min was selected for all scans), the second was uptake times sampled from an academic PET facility with strict adherence to standardization protocols, the third was a distribution similar to scenario 2 but with greater deviation from standards, and the fourth was a mixture of hurried scans (45- to 65-min start of image acquisition) and frequent delays (58- to 115-min uptake time). The proportion of out-of-range scans (<50 or >70 min, or >15-min difference between paired scans) was 0%, 20%, 44%, and 64% for scenarios 1, 2, 3, and 4, respectively. A published SUV correction based on local linearity of uptake-time dependence was applied in a separate analysis. Influence of uptake-time variation was assessed as sensitivity for detecting response (probability of observing a change of ≥30% decrease in (18)F-FDG PET SUV given a true decrease of 40%) and specificity (probability of observing an absolute change of <30% given no true change). RESULTS: Sensitivity was 96% for scenario 1, and ranged from 73% for scenario 4 (95% confidence interval, 70%-76%) to 92% (90%-93%) for scenario 2. Specificity for all scenarios was at least 91%. Single-arm phase II trials required an 8%-115% greater sample size for scenarios 2-4 than for scenario 1. If uptake time is known, SUV correction methods may raise sensitivity to 87%-95% and reduce the sample size increase to less than 27%. CONCLUSION: Uptake-time deviations from standardized protocols occur frequently, potentially decreasing the performance of (18)F-FDG PET response biomarkers. Correcting SUV for uptake time improves sensitivity, but algorithm refinement is needed. Stricter uptake-time control and effective correction algorithms could improve power and decrease costs for clinical trials using (18)F-FDG PET endpoints.
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