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

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

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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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Crossref · 10.1007/s00259-017-3740-2

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Figures

Fig. 1 Number of articles reporting the use of MATV, SUVmax and SUVpeak as a function of year of publication. Articles were identified by Medline search with the following keywords: (MTV OR MATV AND PET), (SUVmax AND PET) or (SUVpeak AND PET). Only human studies were included
Fig. 1 Number of articles reporting the use of MATV, SUVmax and SUVpeak as a function of year of publication. Articles were identified by Medline search with the following keywords: (MTV OR MATV AND PET), (SUVmax AND PET) or (SUVpeak AND PET). Only human studies were includedCC BY · PubMed Central
Fig. 2 Illustration of reconstruction harmonization methods and brief summary of the main factors influencing SUV
Fig. 2 Illustration of reconstruction harmonization methods and brief summary of the main factors influencing SUVCC BY · PubMed Central
Fig. 3 Effect of reconstruction inconsistencies and impact of harmonization on therapy assessment with EORTC response criteria and PERCIST. Relationship between standardized uptake values normalized to lean body mass (SUL)max and SULpeak in lesions extracted from PSF ± TOF (a) or PSF ± TOF.EQ (b) and OSEM images, assessed using Bland-Altman plots. Of note is the greater sensitivity of SUVmax to reconstruction variability, compared to SUVpeak: the number of cases exceeding the threshold to discriminate between SMD and PMD, due to reconstruction inconsistency, is higher for SUVmax. Conversely, PERCIST appears less sensitive than EORTC criteria to reconstruction inconsistency between pre- and post-treatment scans: panel c displays EORTC classification and PERCIST for the standard of reference (OSEMPET1/OSEMPET2) and for other scenarios. d: representative images of a 72-year-old male patient with NSCLC treated by chemotherapy, classified as SMD according to the standard of reference. The use of OSEM for baseline scan and PSF + TOF for post-treatment scan, mimicking a system upgrade during a trial, would lead to PMD classification for both EORTC and PERCIST, while the use of harmonized data would correctly classify the patient
Fig. 3 Effect of reconstruction inconsistencies and impact of harmonization on therapy assessment with EORTC response criteria and PERCIST. Relationship between standardized uptake values normalized to lean body mass (SUL)max and SULpeak in lesions extracted from PSF ± TOF (a) or PSF ± TOF.EQ (b) and OSEM images, assessed using Bland-Altman plots. Of note is the greater sensitivity of SUVmax to reconstruction variability, compared to SUVpeak: the number of cases exceeding the threshold to discriminate between SMD and PMD, due to reconstruction inconsistency, is higher for SUVmax. Conversely, PERCIST appears less sensitive than EORTC criteria to reconstruction inconsistency between pre- and post-treatment scans: panel c displays EORTC classification and PERCIST for the standard of reference (OSEMPET1/OSEMPET2) and for other scenarios. d: representative images of a 72-year-old male patient with NSCLC treated by chemotherapy, classified as SMD according to the standard of reference. The use of OSEM for baseline scan and PSF + TOF for post-treatment scan, mimicking a system upgrade during a trial, would lead to PMD classification for both EORTC and PERCIST, while the use of harmonized data would correctly classify the patientCC BY · PubMed Central
Fig. 4 Results from the EARL electronic survey. Data are displayed as pie charts
Fig. 4 Results from the EARL electronic survey. Data are displayed as pie chartsCC BY · PubMed Central

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Research works printed but not linked: 2

  1. 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
  2. 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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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
ORCID
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
Licence
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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