Literature DB >> 30850971

How Often Do We Fail to Classify the Treatment Response with [18F]FDG PET/CT Acquired on Different Scanners? Data from Clinical Oncological Practice Using an Automatic Tool for SUV Harmonization.

Maria Vittoria Mattoli1, Maria Lucia Calcagni2,3, Silvia Taralli2, Luca Indovina4, Bruce S Spottiswoode5, Alessandro Giordano2,3.   

Abstract

PURPOSE: Tumor response evaluated by 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) with standardized uptake value (SUV) is questionable when pre- and post-treatment PET/CT are acquired on different scanners. The aims of our study, performed in oncological patients who underwent pre- and post-treatment [18F]FDG PET/CT on different scanners, were (1) to evaluate whether EQ·PET, a proprietary SUV inter-exams harmonization tool, modifies the EORTC tumor response classification and (2) to assess which classification (harmonized and non-harmonized) better predicts clinical outcome. PROCEDURES: We retrospectively identified 95 PET pairs (pre- and post-treatment) performed on different scanners (Biograph mCT, Siemens; GEMINI GXL, Philips) in 73 oncological patients (52F; 57.8 ± 16.3 years). An 8-mm Gaussian filter was applied for the Biograph protocol to meet the EANM/EARL harmonization standard; no filter was needed for GXL. SUVmax and SUVmaxEQ of the same target lesion in the pre- and post-treatment PET/CT were noted. For each PET pair, the metabolic response classification (responder/non-responder), derived from combining the EORTC response categories, was evaluated twice (with and without harmonization). In discordant cases, the association of each metabolic response classification with final clinical response assessment and survival data (2-year disease-free survival, DFS) was assessed.
RESULTS: On Biograph, SUVmaxEQ of all target lesions was significantly lower (p = 0.001) than SUVmax (8.5 ± 6.8 vs 12.5 ± 9.6; - 38.6 %). A discordance between the two metabolic response classifications (harmonized and non-harmonized) was found in 19/95 (20 %) PET pairs. In this subgroup (n = 19; mean follow-up, 33.9 ± 9 months), responders according to harmonized classification (n = 9) had longer DFS (47.5 months, 88.9 %) than responders (n = 10) according to non-harmonized classification (26.3 months, 50.0 %; p = 0.01). Moreover, harmonized classification showed a better association with final clinical response assessment (17/19 PET pairs).
CONCLUSIONS: The harmonized metabolic response classification is more associated with the final clinical response assessment, and it is able to better predict the DFS than the non-harmonized classification. EQ·PET is a useful harmonization tool for evaluating metabolic tumor response using different PET/CT scanners, also in different departments or for multicenter studies.

Entities:  

Keywords:  EORTC; EQ·PET; Harmonization; PET; Therapy response; [18F]FDG

Mesh:

Substances:

Year:  2019        PMID: 30850971     DOI: 10.1007/s11307-019-01342-5

Source DB:  PubMed          Journal:  Mol Imaging Biol        ISSN: 1536-1632            Impact factor:   3.488


  23 in total

Review 1.  Standards for PET image acquisition and quantitative data analysis.

Authors:  Ronald Boellaard
Journal:  J Nucl Med       Date:  2009-04-20       Impact factor: 10.057

2.  Does PET Reconstruction Method Affect Deauville Scoring in Lymphoma Patients?

Authors:  Ronald Boellaard; Carsten Kobe; Josée M Zijlstra; N George Mikhaeel; Peter W M Johnson; Stefan Müller; Ulrich Dührsen; Otto S Hoekstra; Sally Barrington
Journal:  J Nucl Med       Date:  2018-04-06       Impact factor: 10.057

3.  How to harmonize SUVs obtained by hybrid PET/CT scanners with and without point spread function correction.

Authors:  Alice Ferretti; Sotirios Chondrogiannis; Lucia Rampin; Elena Bellan; Maria Cristina Marzola; Gaia Grassetto; Stella Gusella; Anna Margherita Maffione; Marcello Gava; Domenico Rubello
Journal:  Phys Med Biol       Date:  2018-11-26       Impact factor: 3.609

4.  Generating harmonized SUV within the EANM EARL accreditation program: software approach versus EARL-compliant reconstruction.

Authors:  Charline Lasnon; Thibault Salomon; Cédric Desmonts; Pascal Dô; Youssef Oulkhouir; Jeannick Madelaine; Nicolas Aide
Journal:  Ann Nucl Med       Date:  2016-11-03       Impact factor: 2.668

5.  What 18F-FDG PET Response-Assessment Method Best Predicts Survival After Curative-Intent Chemoradiation in Non-Small Cell Lung Cancer: EORTC, PERCIST, Peter Mac Criteria, or Deauville Criteria?

Authors:  Guy-Anne Turgeon; Amir Iravani; Tim Akhurst; Alexis Beaulieu; Jason W Callahan; Mathias Bressel; Aidan J Cole; Sarah J Everitt; Shankar Siva; Rodney J Hicks; David L Ball; Michael P Mac Manus
Journal:  J Nucl Med       Date:  2018-07-20       Impact factor: 10.057

Review 6.  From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors.

Authors:  Richard L Wahl; Heather Jacene; Yvette Kasamon; Martin A Lodge
Journal:  J Nucl Med       Date:  2009-05       Impact factor: 10.057

7.  Does PET SUV Harmonization Affect PERCIST Response Classification?

Authors:  Elske Quak; Pierre-Yves Le Roux; Charline Lasnon; Philippe Robin; Michael S Hofman; David Bourhis; Jason Callahan; David S Binns; Cédric Desmonts; Pierre-Yves Salaun; Rodney J Hicks; Nicolas Aide
Journal:  J Nucl Med       Date:  2016-06-09       Impact factor: 10.057

8.  SUVref: reducing reconstruction-dependent variation in PET SUV.

Authors:  Matthew D Kelly; Jerome M Declerck
Journal:  EJNMMI Res       Date:  2011-08-18       Impact factor: 3.138

Review 9.  EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies.

Authors:  Nicolas Aide; Charline Lasnon; Patrick Veit-Haibach; Terez Sera; Bernhard Sattler; Ronald Boellaard
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-06-16       Impact factor: 9.236

Review 10.  Comparison of the EORTC criteria and PERCIST in solid tumors: a pooled analysis and review.

Authors:  Jung Han Kim
Journal:  Oncotarget       Date:  2016-09-06
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  1 in total

1.  Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Authors:  Yang Nan; Javier Del Ser; Simon Walsh; Carola Schönlieb; Michael Roberts; Ian Selby; Kit Howard; John Owen; Jon Neville; Julien Guiot; Benoit Ernst; Ana Pastor; Angel Alberich-Bayarri; Marion I Menzel; Sean Walsh; Wim Vos; Nina Flerin; Jean-Paul Charbonnier; Eva van Rikxoort; Avishek Chatterjee; Henry Woodruff; Philippe Lambin; Leonor Cerdá-Alberich; Luis Martí-Bonmatí; Francisco Herrera; Guang Yang
Journal:  Inf Fusion       Date:  2022-06       Impact factor: 17.564

  1 in total

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