Abstract
PET with the glucose analog 18F-FDG is increasingly used to monitor tumor response to therapy. To use quantitative measurements of tumor 18F-FDG uptake for assessment of tumor response, the repeatability of this quantitative metabolic imaging method needs to be established. Therefore, we determined the repeatability of different standardized uptake value (SUV) measurements using the available data. Methods: A systematic literature search was performed to identify studies addressing 18F-FDG repeatability in malignant tumors. The level of agreement between test and retest values of 2 PET uptake measures, maximum SUV (SUVmax) and mean SUV (SUVmean), was assessed with the coefficient of repeatability using generalized linear mixed-effects models. In addition, the influence of tumor volume on repeatability was assessed. Principal component transformation was used to compare the reproducibility of the 2 different uptake measures. Results: Five cohorts were identified for this metaanalysis. For SUVmax and SUVmean, datasets of 86 and 102 patients, respectively, were available. Percentage repeatability is a function of the level of uptake. SUVmean had the best repeatability characteristics; for serial PET scans, a threshold of a combination of 20% as well as 1.2 SUVmean units was most appropriate. After adjusting for uptake rate, tumor volume had minimal influence on repeatability. Conclusion: SUVmean had better repeatability performance than SUVmax. Both measures showed poor repeatability for lesions with low 18F-FDG uptake. We recommend the evaluation of biologic effects in PET by reporting a combination of minimal relative and absolute changes to account for test–retest variability.
PET with 18F-FDG has gained an important role in the clinical setting to detect and stage malignancies and assess treatment response (1–6). In the research setting, PET is increasingly being used to study early changes of biologic effects during and after anticancer treatment (7–10). The noninvasive nature of PET allows multiple serial measurements without interfering with biologic processes within the tumor and might obviate more invasive procedures, such as biopsy.
Even though PET clinical practice is still dominated by qualitative (visual) image analysis, several potential indications require quantification, for example, when prognostic and predictive information is required beyond the level of TNM staging. A decrease of 18F-FDG uptake after therapy is associated with favorable clinical outcome (1–6,11). However, to date only qualitative, and not quantitative, PET measures have been incorporated in response classification systems, in solid tumors and lymphoma (12,13).
The European Organization for Research and Treatment of Cancer PET Study Group published recommendations, as far back as 1999, for response monitoring using quantitative PET data to promote consistency in the reporting of studies (14). The proposed system was based on the results of both drug evaluation and a few repeatability studies.
To discriminate true signal change from noise and to be able to stratify patients on the basis of changes in 18F-FDG uptake values, the repeatability of the measurement and the error of the determination need to be known. The present metaanalysis determined the repeatability of different SUV measurements using the available data and evaluated potential sources of heterogeneity.
MATERIALS AND METHODS
Study Design
We performed a systematic literature search of Medline and Embase databases to identify studies addressing 18F-FDG repeatability in malignant tumors using the following search terms: PET, FDG, repeatability, and test-retest. Additionally, extensive cross-referencing was done, review articles were screened, and experts in the field were consulted. Studies were included when the following criteria were met: the study assessed the repeatability with 18F-FDG PET in malignant tumors, it used standardized uptake values (SUVs), it used uniform acquisition and reconstruction protocols, and it applied the same scanner for the test and retest scan for each patient (i.e., no within-patient scanner variation).
For dynamic studies, SUVs were calculated using the last frame of the dynamic acquisition. Because the method of tumor delineation (e.g., maximum pixel value or threshold-based or fixed-diameter volume of interest [VOI]) can affect 18F-FDG uptake measures, we attempted to obtain uniformly defined tumor volumes between studies for each uptake measure. This volume was defined by a 3-dimensional threshold-based volume (isocontour-defined, with a cutoff of 50% of the maximum 18F-FDG concentration within the tumor). If we were unable to extract the required data from the original publications, authors were asked to provide the required data or to reanalyze their data with the isocontour technique (using software developed in-house and provided by us).
Statistical Analysis
For both PET uptake measures, maximum SUV (SUVmax) and mean SUV (SUVmean), the level of agreement between test and retest measurements was assessed using the intraclass correlation coefficient. The intraclass correlation coefficient was calculated using a random-effects model with random intercepts for published study, patient, and tumor location. Kruskal–Wallis tests were applied to the uptake measures to assess systematic bias between studies. One-sample Anderson–Darling tests were used to assess the distribution of the means of the test and retest observations.
Variance–Mean and Variance–Volume Relations
To assess repeatability, we determined the relation between the mean and variance of the test and retest scans, where the variance in the test–retest measurements is assumed to be due solely to measurement error. To account for differences in this relation between published studies, we assessed this association using generalized linear mixed-effects models (with published study as a random factor) and generalized linear models (with published study as a fixed effect). The outcome variable was the square of the difference in test–retest measurements, with the log-transformed test–retest mean as a fixed effect. Given the assumption of normality, the square of the difference is χ2-distributed, and therefore γ-error distributions were used (15). The log-link function was used to relate the estimated variance to the test–retest mean, resulting in an allometric mean–variance relation. Differences between published studies were assessed using the generalized linear models with published study as a fixed effect. The influence of tumor volume on 18F-FDG uptake test–retest repeatability was assessed by including the log-transformed tumor volumes as fixed effects. To avoid extrapolation, we limited the estimated variance–mean associations to values between the fifth smallest and fifth largest observed PET measurement values.
Coefficient of 95% (CR95) Repeatability
Once the variance–mean relation had been estimated, the relation between the CR95 repeatability and the mean was calculated as 1.96 times the SD (16,17). The CR95 is the variation solely due to measurement error. If the difference of 2 measurements exceeds the CR95, then this difference is 95% likely to be due to a true change in tumor 18F-FDG uptake rate and not measurement error.
Single PET Observations
The test–retest CR95 corresponds to a 2-observation setting, such as serial baseline and posttreatment assessments. In some clinical trials, however, a single PET scan result is used, for example, for patient stratification during randomization. The equivalent single-observation CR95 can be calculated by dividing the test–retest CR95 by √2 (i.e., half the variance), with the assumption that all measurements are performed on the same PET scanner using the same acquisition and reconstruction protocols.
Comparison of Different Quantitative 18F-FDG Measures
To compare the repeatability of the two 18F-FDG uptake measurements, a principal component analysis was performed using data from the 4 studies that analyzed both PET measures. This analysis resulted in a transformation of each of the measures onto the first principal component and thus allowed their CR95s to be compared on the same scale. The significance level of all tests was set at 0.05. The generalized linear mixed-effects models were fitted using the GLIMMIX procedure in SAS (SAS Institute Inc.). All other analyses were performed in R (version 2.9.2; The R Foundation for Statistical Computing, 2009).
RESULTS
Eight repeatability studies were identified (16,18–24). One study was excluded because of the use of different scanners for the test and retest scan (24). All authors were contacted to provide patient-based data on tumor volume, location, and 18F-FDG uptake. The study characteristics are summarized in Table 1. Nakamoto et al. (22) and Krak et al. (19) were extended analyses of cohorts that were originally described and published by Minn et al. (20) and Hoekstra et al. (18), respectively. Thus, 5 cohorts were available for this metaanalysis. All repeated scans were obtained using the same scanner as previously for each patient. Three studies used a dynamic scan protocol (16,18,20), and the remaining 2 applied a static protocol (21,23). All but 1 (23) were single-center studies. Tumor types were gastrointestinal (23), lung (18,20), and miscellaneous primary, predominantly located in the chest (16,21). Two studies (21,23) used integrated PET/CT scanners; the others a PET-only system. Nahmias et al. (21) applied a 90-min time interval between radiotracer injection and scanning, whereas all other studies applied a 60-min time interval. The multicenter study by Velasquez et al. (23) reported on 2 datasets, 1 before and 1 after quality assurance assessment. The latter dataset was used for the present study. Finally, Minn et al. (20) excluded lesions with a diameter lower than 2 cm, whereas the other studies had no clear restrictions regarding lesion size.
Individual Study Characteristics
Repeatability of SUVs
SUVmax
The 4 available datasets for SUVmax (18,20,21,23) included 163 tumor lesions in 86 patients. The intraclass correlation coefficient of SUVmax was 0.90. The goodness-of-fit tests for normality indicated that SUVmax on the original scale was significantly different from normal (P < 0.0001). After log-transformation, the distribution was normalized (P = 0.37). Figure 1A presents the overall and study-specific relations between the mean and the SD (i.e., variance) of the test and retest scans. The difference in test–retest variability between studies was not significant (P = 0.25). Test–retest variability in absolute terms increases with higher SUVmax (Figs. 1A and 1B), whereas the percentage change decreases with higher SUVmax (Fig. 1C). As a consequence, it is not possible to generate just 1 value that defines the limits of test–retest variability for the full range of SUVmax; however, this dilemma can be overcome by combining an absolute and a percentage change of SUVmax. From Figure 1C, these values can be extracted. The required absolute difference is calculated as the relative difference multiplied by the mean value where the CR95 curve crosses the relative difference value. For SUVmax, the 30% relative difference crosses the CR95 at an SUVmax of 6.7. Thus, the combination of an absolute change in SUVmax of more than 2 units (0.3 × 6.7) and a relative change of more than 30% is more than 95% likely to be a true change rather than a measurement error, for any SUVmax that was encountered in this study. From the same figure, it can be calculated that a change of more than 3.1 units and 25% relative change also exceeds 95% test–retest variability. The combinations of the required minimal absolute and percentage changes that are needed to exceed 95% test–retest variability for the SUV parameters are presented in Table 2.
Repeatability of SUVmax. (A) Estimated study-specific SD (colored dashed lines; study as fixed effect) and overall SD (black solid lines; study as random effect). (B) Test and retest scan values plotted on original scale. Solid line is coefficient of repeatability (CR95). (C) Relation between CR95, as percentage change, with level of SUVmax. Dotted lines indicate absolute and relative differences, as presented in Table 2.
Required Relative and Absolute Differences to Exceed Test–Retest Variability
SUVmean
The 5 available datasets for SUVmean (16,18,20,21,23) included 213 tumor lesions in 102 patients. The tumor delineation methods differed between the 5 available studies. Hoekstra et al. (18) and Weber et al. (16) delineated isocontour-defined volumes with a 50% threshold of the maximum voxel value. Nahmias et al. (21) originally published manually defined volumes but reanalyzed their data on our request using the 50% isocontour technique. Velasquez et al. (23) used a 70% threshold technique because it was not possible to define 50% volumes without contaminating the tumor VOI with background tissue (because of 18F-FDG uptake heterogeneity and a relatively low tumor-to-background contrast). Minn et al. (20) delineated tumor volumes as 4 × 4 voxels around the voxel with maximum 18F-FDG uptake.
The intraclass correlation coefficient of SUVmean was 0.91. The goodness-of-fit tests for normality indicated that SUVmean on the original scale was significantly different from normal (P = 0.002). After log-transformation this was normalized (P = 0.77).
Figure 2A presents the overall and study-specific relations between the mean and the SD (i.e., variance) of the test and retest scans. These associations differed significantly across studies (P < 0.0001), with the greatest variance observed in the study of Velasquez et al. (23) and the lowest in the study of Nahmias et al. (21) (manual VOI delineation). When only studies applying the 50% isocontour technique were included (Hoekstra et al. (18), Weber et al. (16), and 50% isocontour data of Nahmias et al. (21)), no difference in test–retest variability was apparent between the studies (P = 0.13; Fig. 3). In the dataset of Nahmias et al. (21), the manual delineation method performed better in terms of repeatability than the automated method (P = 0.0001).
Repeatability of SUVmean. (A) Estimated study-specific SD (colored dashed lines; study as fixed effect) and overall SD (black solid lines; study as random effect). (B) Test and retest scan values plotted on original scale. Solid line is coefficient of repeatability (CR95). (C) Relation between CR95, as percentage change, with level of SUVmean. Dotted lines indicate absolute and relative differences, as presented in Table 2.
Repeatability of SUVmean using 50% isocontouring. (A) Estimated study-specific SD (colored dashed lines; study as fixed effect) and overall SD (black solid lines; study as random effect). (B) Test and retest scan values plotted on original scale. Solid line is coefficient of repeatability (CR95). (C) Relation between CR95, as percentage change, with level of SUVmean using 50% isocontouring. Dotted lines indicate absolute and relative differences, as presented in Table 2.
Figure 2 for SUVmean shows the same trend as was observed for SUVmax. The absolute change in test–retest variability increases with higher SUVmean (Figs. 2A and 2B), whereas the percentage change decreases with higher SUVmean (Fig. 2C). As a consequence, a combination of an absolute and a percentage change is necessary to cover the limits of test–retest variability for the full range of SUVmean. From Figure 2, it can be extracted that the combined change in SUVmean of more than 1.2 units and 20% exceeded 95% test–retest variability for any SUVmean that was encountered in this study, irrespective of the tumor delineation method (values are presented in Table 2). Homogeneous tumor delineation (50% threshold technique) resulted in lower test–retest variability (Fig. 3; Table 2).
We found no statistically significant difference between studies using static (Nahmias et al. (21) and Velasquez et al. (23)) as compared with dynamic PET techniques (Hoekstra et al. (18), Minn et al. (20), and Weber et al. (16)) (P = 0.90). The principal component analysis indicated that the test–retest variability of SUVmax was larger than that of SUVmean (Supplemental Figs. 1 and 2; supplemental materials are available online only at http://jnm.snmjournals.org).
Single-Assessment Setting
By definition, the CR95 for 1 observation is smaller than the equivalent 2 observation values because of fewer sources of measurement error. The single-assessment CR95s were within 25% change for SUVmax and within 20% for SUVmean (Supplemental Table 1; Supplemental Fig. 2).
Impact of Tumor Volume on Repeatability
Tumor volume was moderately correlated with the 18F-FDG uptake level (Table 3; Supplemental Fig. 3A). However, uptake level was a better indicator for changes in test–retest variability than tumor volume for both parameters (Table 3). Repeatability of SUVmean seems to be relatively unaffected by tumor volume, irrespective of the delineation method (Table 3; Supplemental Fig. 3B).
Correlations Between 18F-FDG Uptake and Tumor Volume and Their Influence on Test–Retest Variability
DISCUSSION
This metaanalysis summarizes the published evidence on the repeatability of commonly used quantitative 18F-FDG measurements in oncology. Our results apply to the use of PET in both serial- and single-assessment settings. Compared with SUVmax, SUVmean had the better repeatability.
For both measures, the percentage change in test–retest variability was not constant across the range of parameter values and negatively related to the level of 18F-FDG uptake. In a clinical setting, combining a minimal relative and absolute change is sufficient to define a (biologic) effect that cannot be explained by measurement error only (Table 2 shows thresholds). In the context of analyzing serial 18F-FDG uptake changes, the PET response criteria in solid tumors (PERCIST) classification system assumes a biologic change with peak SUV (SUVpeak) changes greater than 30% in combination with 0.8 unit change of absolute SUVpeak (25). In the present metaanalysis, there were insufficient data to explore SUVpeak. Numeric values of SUVpeak will typically vary between SUVmax and SUVmean (if VOIs of SUVmean exceed the SUVpeak VOIs). Figure 4 corroborates the PERCIST assumption of combining 30% with 0.8 unit SUVpeak (for SUVmean, we found 30% and 0.75). In addition, Figure 4 shows that, when using SUVmean, a minimal relative change of 20% in combination with 1.2 unit change will also represent a biologic change.
Relation between CR95, as percentage change, with level of SUVmean using 50% isocontouring. Cutoff rules were at 20% and 30% relative change, with associated absolute changes for 3 studies. Required absolute difference is calculated as relative difference multiplied by mean value, where CR95 curve crosses relative difference value. For example, CR95 equals 30% at 2.2; hence, associated absolute difference is 0.66 (2.2 × 0.3). Therefore, likelihood is less than 5% that change of at least 30% and more than 0.66 units is due to measurement error in 18F-FDG uptake rate. Blue dashed lines represent areas of combined coverage for each rule.
The first combination is advantageous for tumors with low uptake values in which small differences in absolute values correspond to large differences in percentage change. The latter combination is advantageous for high uptake values for which relatively large differences in absolute values correspond to relatively small differences in percentage change. It is probable that for tumors with extremely high uptake values, an even smaller relative change threshold would be appropriate. We are unable to suggest corresponding absolute change values because these uptake values are outside the range of our data.
Alternatively, to select lesions for evaluation with 18F-FDG PET, a minimal SUV could be applied (see figures for trade-off values). This selection simplifies the situation by excluding low uptake values with large (relative change) test–retest variability, leaving the remaining values with a consistent relative test–retest change that can be used as a (1 value) cutoff for response assessment. The disadvantage of this approach is that increases in 18F-FDG uptake (e.g., due to disease progression) can be measured reliably, but decreases to values below the threshold (e.g., due to treatment response) cannot.
Even though methodologies were not fully consistent throughout the studies, we found no important interstudy differences in parameter repeatability. For SUVmax, no difference was found at all, whereas for SUVmean a difference was present only when all studies were included, irrespective of the delineation method used. This difference was caused by a lower performance of the Velasquez data and good performance of the manually delineated data of Nahmias et al. (21). Velasquez et al. (23) applied a multicenter design with a large number of centers. Also, the patient population was substantially different from that of the other studies, because only patients with advanced gastrointestinal malignancies were included by Velasquez et al., whereas the other studies predominantly evaluated lung lesions (Table 1). Tumor delineation in the liver and gastrointestinal tract may have been challenged by physiologic uptake, which is higher and more variable there than in the lung. Furthermore, spillover of activity from normal organs with variable 18F-FDG uptake (e.g., kidneys, bowel loops) may have affected the test–retest variability. The authors reported that they were forced to apply a higher threshold of the maximum voxel value (70%) to enable tumor tissue discrimination from the normal background. Earlier work from our center showed that test–retest variability increases with higher thresholds of the maximum tumor volume (19). Possibly these factors resulted in lower repeatability.
Therefore, we performed a subset analysis of homogeneously delineated lesions using the 50% isocontour technique. This inherently excluded the data of Velasquez et al. (23), resulting in interstudy homogeneity. Better repeatability was found for this subset, as presented in Figure 3 and Table 2. It remains elusive whether the worse repeatability in the overall dataset is due to the delineation technique (70% threshold) or the location of the lesions (abdominal). Therefore, the cutoff values found for the 50% threshold subset hold only for 50% isocontoured extraabdominal lesions, whereas the overall results can be used for all organ sites and histology and irrespective of the SUVmean tumor delineation method.
Interestingly, the manually delineated data of Nahmias et al. (21) performed better than the threshold analysis of the same dataset. This better performance was also reported earlier by our group (19). However, both studies were single-observer, whereas the strength of the semiautomated technique is the high intra- and interobserver repeatability for lesion delineation. Therefore, superiority can be evaluated only by performing a head-to-head analysis in a multiobserver setting.
In previous studies, it was suggested that repeatability is a function of 18F-FDG uptake value and tumor volume. Weber et al. (16) reported that the absolute difference between the test and retest values remained constant across the range of parameter value but that the relative change increased with decreasing uptake value. Nahmias et al. (21) also reported that the absolute difference of the mean uptake value was constant across the range of SUVmean, with increasing relative change for lower SUVmean. However, this group reported that for SUVmax the absolute difference increased with increasing SUVmax, whereas the relative change remained constant.
In the pooled dataset of the current study, mean uptake was a better indicator for changes in test–retest variability than tumor volume (Table 3). SUV repeatability seemed to be relatively unaffected by VOI size.
We observed a moderately positive correlation between the uptake value and tumor volume (Table 3; Supplemental Fig. 3A). Hypothetically this can, at least in part, be explained by the partial-volume phenomenon, which results in an underestimation of 18F-FDG uptake in smaller lesions, in turn resulting in more difficult tumor delineation because of lower tumor-to-background contrast (26).
Our results apply to the setting in which the same scanner is being used for both the baseline and the posttreatment scans, with the same acquisition, reconstruction, data processing, and analytic protocol. These inclusion criteria were mandatory because variation in these parameters can result in parameter differences of up to 50%, representing only noise and not a true biologic change (27,28). Although interstudy differences in image contrast and resolution remained, their effects on repeatability were negligible (29). Whether this is also true for newer systems with better spatial resolution cannot be predicted on the basis of the current data.
The study by Kamibayashi et al. (24) was excluded because of the use of a different PET scanner for the test and retest scans. In this study, tumor SUV test–retest variability was evaluated in patients who were scanned once on a PET-only scanner and once on a PET/CT scanner without treatment intervention between the 2 scans. The scanners were from the same vendor, and the same acquisition and analytic protocol was used for both scans. The authors applied a manually drawn 2-dimenstional region of interest for tumor delineation. The SD was only slightly worse than that of the other single-center repeatability studies and comparable to the multicenter study of Velasquez et al. (23), with an SD of 12% ± 10.2% for SUVmean and 16.1% ± 10.5% for SUVmax. This result might indicate that different scanners can be used at baseline and after treatment, provided that standardization of acquisition, reconstruction, data processing, and analysis is applied. Whether this possibility also holds for semiautomatic VOI definition and the use of PET scanners from different vendors remains to be further examined.
Although no statistical interstudy difference was present after the data were pooled and VOI definition heterogeneity was corrected, the cutoff values for definition of test–retest variability should be assessed with care because some methodologic and patient spectrum heterogeneity between studies prevailed.
However, we believe that this metaanalysis provides the most critical and robust view of 18F-FDG PET repeatability in the oncologic setting to date. It enabled the evaluation of dependency on mean uptake value and tumor volume, issues that were open for discussion since the publication of Weber's study in 1999 (16). Ideally, these results should be confirmed prospectively, preferably in a large multicenter study. Possibly, studies such as the “FDG PET/CT as a Predictive Marker of Tumor Response and Patient Outcome: Prospective Validation in Non-Small Cell Lung Carcinoma” (American College of Radiology Imaging Network study 6678), incorporating a test–retest study evaluating 18F-FDG PET/CT with a static protocol at 60 min after injection, can provide the opportunity for this confirmation.
CONCLUSION
This metaanalysis shows the repeatability of different 18F-FDG uptake measurements using the available data. SUVmean performed better than SUVmax. Importantly, both parameters showed worse repeatability for lesions with low 18F-FDG uptake, which can be accounted for by combining relative and absolute differences. For SUVmean, a 30% and 0.75 unit change or 20% and 1.2 unit change exceed 95% test–retest variability. Homogeneous delineation and exclusion of abdominal lesions improved repeatability. For SUVmax, the equivalent values were 30% and 2 units change. These cutoff values can serve as a guide for future clinical trials. Given the limited data and because some interstudy heterogeneity prevailed, our results should preferably be confirmed in a prospective repeatability study, preferentially including SUVpeak. For serial (e.g., baseline and postintervention scans) and single PET measurements, different thresholds should be used because each scan is affected by test–retest noise.
DISCLOSURE STATEMENT
The costs of publication of this article were defrayed in part by the payment of page charges. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.
Acknowledgments
We are grateful to Drs. C. Nahmias and L.M. Wahl for performing reanalyses of their published study results. No potential conflict of interest relevant to this article was reported.
Footnotes
Published online Apr. 10, 2012.
- © 2012 by the Society of Nuclear Medicine, Inc.
REFERENCES
- Received for publication July 6, 2011.
- Accepted for publication January 3, 2012.
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