Cited by 344Medical Imaging Techniques and ApplicationsLung Cancer Diagnosis and TreatmentRadiomics and Machine Learning in Medical Imaging

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EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies

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Aide, Nicolas, Lasnon, Charline, Veit-Haibach, Patrick, Sera, Terez, Sattler, Bernhard, Boellaard, Ronald·2017·European Journal of Nuclear Medicine and Molecular Imaging

DOI 10.1007/s00259-017-3740-2PMC PMC5541084

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Abstract

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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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
ORCID: Aide, Nicolas
Lasnon, Charline
Sattler, Bernhard
Boellaard, Ronald
Venue: European Journal of Nuclear Medicine and Molecular Imaging
Publisher: Springer Science and Business Media LLC
Published: 2017-06-16
Volume: 44
Issue: S1
Pages: 17-31
ISSN: 1619-7070 (ISSN-L)
1619-7089
DOI: 10.1007/s00259-017-3740-2
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (unspecified)
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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EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies

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