The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies
Open accessAbstract
Quantitative positron emission tomography/computed tomography (PET/CT) can be used as diagnostic or prognostic tools (i.e. single measurement) or for therapy monitoring (i.e. longitudinal studies) in multicentre studies. Use of quantitative parameters, such as standardized uptake values (SUVs), metabolic active tumor volumes (MATVs) or total lesion glycolysis (TLG), in a multicenter setting requires that these parameters be comparable among patients and sites, regardless of the PET/CT system used. This review describes the motivations and the methodologies for quantitative PET/CT performance harmonization with emphasis on the EANM Research Ltd. (EARL) Fluorodeoxyglucose (FDG) PET/CT accreditation program, one of the international harmonization programs aiming at using FDG PET as a quantitative imaging biomarker. In addition, future accreditation initiatives will be discussed. The validation of the EARL accreditation program to harmonize SUVs and MATVs is described in a wide range of tumor types, with focus on therapy assessment using either the European Organization for Research and Treatment of Cancer (EORTC) criteria or PET Evaluation Response Criteria in Solid Tumors (PERCIST), as well as liver-based scales such as the Deauville score. Finally, also presented in this paper are the results from a survey across 51 EARL-accredited centers reporting how the program was implemented and its impact on daily routine and in clinical trials, harmonization of new metrics such as MATV and heterogeneity features.
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“This has undermined any attempts to establish widely adoptable SUV thresholds unless rigorous harmonization measures are followed [101,103,181].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [181]
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From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors
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Self-citationFDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0
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Self-citationStandards for PET Image Acquisition and Quantitative Data Analysis
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Self-citationPerformance Measurements of the Siemens mMR Integrated Whole-Body PET/MR Scanner
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Research works printed but not linked: 2
- Quak E, Le Roux PY, Hofman MS, Robin P, Bourhis D, Callahan J, et al. Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients. Eur J Nucl med Mol Imaging. 2015;42:2072–82. doi: 10.1007/s00259-015-3128-0 . · DOI · Paper page
- Barrington SF, Kluge R. FDG-PET for therapy monitoring in Hodgkin and non-Hodgkin lymphoma. Eur J Nucl Med Mol Imaging. 2017. doi: 10.1007/s00259-017-3690-8 . · DOI
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FDG PET/CT for assessing tumour response to immunotherapy
Aide, N., Hicks, R. J., Tourneau, C. L., Lheureux, S., Fanti, S., & Lopci, E. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4171-4 · Cited by 266
Self-citationPapadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226
Quantification, improvement, and harmonization of small lesion detection with state-of-the-art PET
van der Vos, C. S., Koopman, D., Rijnsdorp, S., Arends, A. J., Boellaard, R., van Dalen, J. A., Lubberink, M., Willemsen, A. T. M., & Visser, E. P. (2017) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3727-z · Cited by 206
Self-citationHarmonization strategies for multicenter radiomics investigations
Da‐ano, R., Visvikis, D., & Hatt, M. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aba798 · Cited by 167
Albert, N. L., Galldiks, N., Ellingson, B. M., van den Bent, M. J., Chang, S. M., Cicone, F., de Groot, J., Koh, E., Law, I. G., Rhun, É. L., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(23)00525-9 · Cited by 198
Metadata
- Authors
- Aide, N., Lasnon, C., Veit-Haibach, P., Sera, T., Sattler, B., & Boellaard, R.
- Affiliations
- Inserm (FR)
- University Hospital Leipzig (DE)
- University Medical Center Groningen (NL)
- Corresponding author
- Aide, Nicolas
- Venue
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2017-06-16
- Volume
- 44
- Issue
- S1
- Pages
- 17-31
- ISSN
- 1619-7070 (ISSN-L)
- 1619-7089
- PMC
- PMC5541084
- OpenAlex
- W2625583840
- Cited by
- 344
- References
- 64
- Citations by year
- 2026: 37 · 2025: 49 · 2024: 37 · 2023: 33 · 2022: 30 · 2021: 54 · 2020: 32 · 2019: 26 · 2018: 33 · 2017: 12
- FWCI
- 4.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- PET quantification, method harmonization, FDG PET/CT, standardized uptake value, total lesion glycolysis, therapy response assessment, EORTC criteria, PERCIST, Deauville score, imaging biomarkers
- MeSH terms
- Humans, Magnetic Resonance Imaging, Neoplasms, Reference Standards, Societies, Medical, Multicenter Studies as Topic, Positron-Emission Tomography
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