Positron emission tomography (PET) is a quantitative imaging modality, but the computation of standardized uptake values (SUVs) requires several instruments to be correctly calibrated. Variability in the calibration process may lead to unreliable quantitation. Sealed source kits containing traceable amounts of [Formula: see text] were used to measure signal stability for 19 PET scanners at nine hospitals in the National Cancer Institute's Quantitative Imaging Network. Repeated measurements of the sources were performed on PET scanners and in dose calibrators. The measured scanner and dose calibrator signal biases were used to compute the bias in SUVs at multiple time points for each site over a 14-month period. Estimation of absolute SUV accuracy was confounded by bias from the solid phantoms' physical properties. On average, the intrascanner coefficient of variation for SUV measurements was 3.5%. Over the entire length of the study, single-scanner SUV values varied over a range of 11%. Dose calibrator bias was not correlated with scanner bias. Calibration factors from the image metadata were nearly as variable as scanner signal, and were correlated with signal for many scanners. SUVs often showed low intrascanner variability between successive measurements but were also prone to shifts in apparent bias, possibly in part due to scanner recalibrations that are part of regular scanner quality control. Biases of key factors in the computation of SUVs were not correlated and their temporal variations did not cancel out of the computation. Long-lived sources and image metadata may provide a check on the recalibration process.
Clinical trials that evaluate cancer treatments may benefit from positron emission tomography (PET) imaging, which for many cancers can discriminate between effective and ineffective treatments. However, the image metrics used to quantify disease and evaluate treatment may be biased by many factors related to clinical protocols and PET system settings, many of which are site- and/or manufacturer-specific. An observational study was conducted using two surveys that were designed to record key sources of bias and variability in PET imaging. These were distributed to hospitals across the United States. The first round of surveys was designed and distributed by the American College of Radiology's Centers of Quantitative Imaging Excellence program in 2011. The second survey expanded on the first and was completed by the National Cancer Institute's Quantitative Imaging Network. Sixty-three sites responded to the first survey and 36 to the second. Key imaging parameters varied across participating sites. The range of reported methods for image acquisition and reconstruction suggests that signal biases are not matched between sites. Patient preparation was also inconsistent, potentially contributing additional variability. For multicenter clinical trials, efforts to control biases through standardization of imaging procedures should precede patient measurements.
Background: Metabolic activity in lesions, measured by FDG-PET, is often used for assessing tumor aggressiveness and response to therapy. Patients may be scanned on different machines, so quantitative measurements should be reproducible. Reducing SUV variability in PET machines throughout a local network can aid in monitoring patient response to therapy and increase access to clinical trials. Methods: Eighteen female patients with advanced or metastatic breast cancer underwent paired FDG PET/CT test-retest studies with 1-15 days between scans, and without interim change in treatment. Ten patients were studied in the same scanner and 8 patients were studied in 2 different scanners. Five different PET/CT scanners were used (2 GE DSTE, 2 Siemens (BioGraph 6 and mCT), 1 Philips Ingenuity TF). Each PET/CT scanner was calibrated using NIST-traceable reference sources to characterize and reduce variability. All of the images were interpreted by two separate reviewers. SUVmax values in lesions, corresponding normal tissue, and normal liver were collected. Linear mixed models with random intercept (patient effects) were fitted to compare differences in log(|SUVmax % difference|+.01) in multiple lesions per patient. Results: SUVmax was assessed in a total of 130 lesions (75 bone). The median number of lesions per patient was 5 (range 1-17). Average SUVmax ranged from 1.0 to 18.2 (mean±SD = 6.0±3.2). The median SUVmax difference was 0.4 (8%) for 47 lesions imaged twice in the same scanner, and was 0.6 (13%) for 83 lesions imaged in two different scanners. In a multivariable linear mixed effects model, SUVmax for different scanners within the same institution did not differ more than for the same scanner (p=0.39), but repeat scans with different scanners and site personnel at had an average of 78% greater percentage difference in SUVmax than for the same scanner (p=0.009). In the same model, the average percent difference in SUVmax for bone lesions was estimated as 30% lower than for other sites (p=0.06, 95% confidence interval 0-50%). Examining normal liver uptake, the median SUVmean was 2.5 (range 1.9-3.1) with an median 6.5% difference between measurements (range 1.1%-23.7%) that did not appear to differ based on scanners used for repeat measurements (p=0.47). Conclusions: The variability in quantitative FDG SUVmax between scans is modest, suggesting reliable reproducibility in appropriately calibrated settings. In our study, bone lesions had somewhat higher fidelity than other tumor sites. Additional studies will address variability in other cancer types. Careful calibration and monitoring of PET/CT scanners, and consistent imaging protocols are necessary in clinical trials that utilize quantitative PET/CT imaging in order to confidently interpret results. Research Support: NIH grant U01-CA148131 and NCI-SAIC Contract 24XS036-004. Citation Format: Linden HM, Peterson LM, Kurland B, Roberts T, Specht J, Shields AT, Novakova A, Christopfel R, Byrd D, Muzi M, Mankoff DA, Kinahan P. Test-retest fidelity of FDG SUVmax in bone and non-boney metastatic breast cancer lesions in local area network PET/CT scanners [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-02-05.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.