Bias in PET Images of Solid Phantoms Due to CT-Based Attenuation Correction

Patients with metastatic melanoma often receive 18F-FDG PET/CT scans on different scanners throughout their monitoring period. In this study, we quantified the impact of scanner harmonization on longitudinal changes in PET standardized uptake values using various harmonization and normalization methods, including an anthropomorphic PET phantom. Twenty metastatic melanoma patients received at least two FDG PET/CT scans, each on two different scanners with an average of 4 months (range: 2–8) between. Scans from a General Electric (GE) Discovery 710 PET CT−1 were harmonized to the GE Discovery VCT using image reconstruction settings matching recovery coefficients in an anthropomorphic phantom with bone equivalent inserts and wall-less synthetic lesions. In patient images, SUVmax was measured for each melanoma lesion and time-point. Lesions were classified as progressing, stable, or responding based on pre-defined threshold of ±30% change in SUVmax. For comparison, harmonization was also performed using simpler methods, including harmonization using a NEMA phantom, post-reconstruction filtering, reference region normalization of SUVmax, and use of SUVpeak instead of SUVmax. In the 20 patients, 90 lesions across two time-points were available for treatment response assessment. Treatment response classification changed in 47% (42/90) of cases after harmonization with anthropomorphic phantom. Before harmonization, 37% (33/90) of the lesions were classified as stable (changing less than 30% between two time-points), while the fraction of stable lesions increased to 58% (52/90) after harmonization. Harmonization with the NEMA phantom agreed with harmonization with the anthropomorphic phantom in 91% (82/90) of cases. Post-reconstruction filtering agreed with anthropomorphic phantom-based harmonization in 83% (75/90) cases. The utilization of reference regions for normalization or SUVpeak was unable to correct for changes as identified by the anthropomorphic phantom-based harmonization. Overall, PET scanner harmonization has a major impact on individual lesion treatment response classification in metastatic melanoma patients. Harmonization using the NEMA phantom yielded similar results to harmonization using anthropomorphic phantom, while the only acceptable post-reconstruction technique was post-reconstruction filtering. Phantom-based harmonization is therefore strongly recommended when comparing lesion uptake across time-points when the images have been acquired on different PET scanners.

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Impact of scanner harmonization on PET-based treatment response assessment in metastatic melanoma

Weisman

Bradshaw²,

Namías³

et al. 2020

Phys. Med. Biol.

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Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

et al. 2020

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The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

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Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint

Nordstrom¹

2020

Tomography

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The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.

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Bias in PET Images of Solid Phantoms Due to CT-Based Attenuation Correction

Cited by 3 publications

References 16 publications

Impact of scanner harmonization on PET-based treatment response assessment in metastatic melanoma

Impact of scanner harmonization on PET-based treatment response assessment in metastatic melanoma

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

Quantitative Imaging Enters the Clinical Arena: A Personal Viewpoint

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