Abstract
BackgroundSeveral 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.MethodsTo 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.ResultsIn 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.ConclusionsDefining patient-specific, baseline dependent cut-off values based on the (null) 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.
Highlights
Several reproducibility studies have established good test-retest reliability of FDG-PET in various oncology settings
Our test data set consisted of 57 2nd/3rd line non-small cell lung cancer (NSCLC) patients receiving erlotinib
All scans were measured by a single reviewer who recorded SUVmax values for up to 5 target lesions per patient with an overall focus on lesions that best represented a patient’s burden of disease
Summary
Several reproducibility studies have established good test-retest reliability of FDG-PET in various oncology settings. 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. FDG-PET is becoming increasingly important as a tool for assessing early treatment effects in clinical trials of novel oncology drugs [1]. Our method is based on the detailed noise distribution derived from of test-retest data and accounts for multiple target lesions in a rigorous statistical manner
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