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Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer

  • Benjamin Houdu
  • Charline Lasnon
  • Idlir Licaj
  • Guy Thomas
  • Pascal Do
  • Anne-Valerie Guizard
  • Cédric Desmonts
  • Nicolas Aide
Original Article
  • 9 Downloads

Abstract

Background

To determine EARL-compliant prognostic SUV thresholds in a mature cohort of patients with locally advanced NSCLC, and to demonstrate how detrimental it is to use a threshold determined on an older-generation PET system with a newer PET/CT machine, and vice versa, or to use such a threshold with non-harmonized multicentre pooled data.

Materials and methods

This was a single-centre retrospective study including 139 consecutive stage IIIA-IIIB patients. PET data were acquired as per the EANM guidelines and reconstructed with unfiltered point spread function (PSF) reconstruction. Subsequently, a 6.3 mm Gaussian filter was applied using the EQ.PET (Siemens Healthineers) methodology to meet the EANM/EARL harmonizing standards (PSFEARL). A multicentre study including non-EARL-compliant systems was simulated by randomly creating four groups of patients whose images were reconstructed with unfiltered PSF and PSF with Gaussian post-filtering of 3, 5, and 10 mm. Identification of optimal SUV thresholds was based on a two-fold cross-validation process that partitioned the overall sample into learning and validation subsamples. Proportional Cox hazards models were used to estimate age-adjusted and multivariable-adjusted hazard ratios (HRs) and their 95% confidence intervals. Kaplan–Meier curves were compared using the log rank test.

Results

Median follow-up was 28 months (1–104 months). For the whole population, the estimated overall survival rate at 36 months was 0.39 [0.31–0.47]. The optimal SUVmax cutoff value was 25.43 (95% CI: 23.41–26.31) and 8.47 (95% CI: 7.23–9.31) for the PSF and for the EARL-compliant dataset respectively. These SUVmax cutoff values were both significantly and independently associated with lung cancer mortality; HRs were 1.73 (1.05–2.84) and 1.92 (1.16–3.19) for the PSF and the EARL-compliant dataset respectively. When (i) applying the optimal PSF SUVmax cutoff on an EARL-compliant dataset and the optimal EARL SUVmax cutoff on a PSF dataset or (ii) applying the optimal EARL compliant SUVmax cutoff to a simulated multicentre dataset, the tumour SUVmax was no longer significantly associated with lung cancer mortality.

Conclusion

The present study provides the PET community with an EARL-compliant SUVmax as an independent prognosticator for advanced NSCLC that should be confirmed in a larger cohort, ideally at other EARL accredited centres, and highlights the need to harmonize PET quantitative metrics when using them for risk stratification of patients.

Keywords

Non-small-cell lung cancer Survival FDG PET/CT Prognosticator Harmonization EARL accreditation program 

Notes

Compliance with ethical standards

Ethical approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Competing interests

None to declare.

Informed consent

In accordance with European regulations, French observational studies without any additional therapy or monitoring procedure do not need the approval of an ethical committee. Nonetheless, we sought approval to collect data for our study from the national committee for data privacy, the National Commission on Informatics and Liberty (CNIL), with the registration n°2,080,317 v 0.

Supplementary material

259_2018_4151_MOESM1_ESM.docx (14 kb)
Supplemental Table 1 (DOCX 13 kb)
259_2018_4151_MOESM2_ESM.docx (15 kb)
Supplemental Table 2 (DOCX 14 kb)
259_2018_4151_Fig4_ESM.png (264 kb)
Supplemental Fig. 1

Recovery coefficients based on maximum pixel values in a NEMA NU2 phantom scanned as per the EANM/EARM accreditation program. PSF with a 3 Gaussian filter falls meets the requirements of a potential update of the EARL-compliant specifications, which are shown using dotted black lines. (PNG 264 kb)

259_2018_4151_MOESM3_ESM.tiff (5.4 mb)
High Resolution Image (TIFF 5573 kb)
259_2018_4151_Fig5_ESM.png (170 kb)
Supplemental Fig. 2

Overall survival Kaplan–Meier curve for an optimal SUVmax cutoff value meeting the harmonizing standards of updated EARL specifications. (PNG 170 kb)

259_2018_4151_MOESM4_ESM.tiff (10.3 mb)
High Resolution Image (TIFF 10577 kb)

References

  1. 1.
    Madsen PH, Holdgaard PC, Christensen JB, Hoilund-Carlsen PF. Clinical utility of F-18 FDG PET-CT in the initial evaluation of lung cancer. Eur J Nucl Med Mol Imaging. 2016;43:2084–97.  https://doi.org/10.1007/s00259-016-3407-4.CrossRefGoogle Scholar
  2. 2.
    Mac Manus MP, Everitt S, Bayne M, Ball D, Plumridge N, Binns D, et al. The use of fused PET/CT images for patient selection and radical radiotherapy target volume definition in patients with non-small cell lung cancer: results of a prospective study with mature survival data. Radiother Oncol. 2013;106:292–8.  https://doi.org/10.1016/j.radonc.2012.12.018.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Hicks RJ. Role of 18F-FDG PET in assessment of response in non-small cell lung cancer. J Nucl Med. 2009;50(Suppl 1):31S–42S.  https://doi.org/10.2967/jnumed.108.057216.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Eschmann SM, Friedel G, Paulsen F, Reimold M, Hehr T, Budach W, et al. Is standardised (18)F-FDG uptake value an outcome predictor in patients with stage III non-small cell lung cancer? Eur J Nucl Med Mol Imaging. 2006;33:263–9.  https://doi.org/10.1007/s00259-005-1953-2.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Aide N, Lasnon C, Veit-Haibach P, Sera T, Sattler B, Boellaard R. EANM/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. Eur J Nucl Med Mol Imaging. 2017;44:17–31.  https://doi.org/10.1007/s00259-017-3740-2.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kaalep A, Sera T, Oyen W, Krause BJ, Chiti A, Liu Y, et al. EANM/EARL FDG-PET/CT accreditation — summary results from the first 200 accredited imaging systems. Eur J Nucl Med Mol Imaging. 2018;45:412–22.  https://doi.org/10.1007/s00259-017-3853-7.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Lasnon C, Salomon T, Desmonts C, Do P, Oulkhouir Y, Madelaine J, et al. Generating harmonized SUV within the EANM EARL accreditation program: software approach versus EARL-compliant reconstruction. Ann Nucl Med. 2017;31:125–34.  https://doi.org/10.1007/s12149-016-1135-2.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    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.  https://doi.org/10.1007/s00259-015-3128-0.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.  https://doi.org/10.1007/s00259-014-2961-x.CrossRefGoogle Scholar
  10. 10.
    Diouf M, Bonnetain F, Barbare JC, Bouche O, Dahan L, Paoletti X, et al. Optimal cut points for Quality of Life Questionnaire–core 30 (QLQ–C30) scales: utility for clinical trials and updates of prognostic systems in advanced hepatocellular carcinoma. Oncologist. 2015;20:62–71.  https://doi.org/10.1634/theoncologist.2014-0175.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Mazumdar M, Glassman JR. Categorizing a prognostic variable: review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med. 2000;19:113–32.CrossRefPubMedCentralGoogle Scholar
  12. 12.
    Faraggi D, Simon R. A simulation study of cross-validation for selecting an optimal cutpoint in univariate survival analysis. Stat Med. 1996;15:2203–13.  https://doi.org/10.1002/(sici)1097-0258(19961030)15:20<2203::aid-sim357>3.0.co;2-g.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Hollander N, Sauerbrei W, Schumacher M. Confidence intervals for the effect of a prognostic factor after selection of an ‘optimal’ cutpoint. Stat Med. 2004;23:1701–13.  https://doi.org/10.1002/sim.1611.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Graham MM, Wahl RL, Hoffman JM, Yap JT, Sunderland JJ, Boellaard R, et al. Summary of the UPICT Protocol for 18F-FDG PET/CT Imaging in Oncology Clinical Trials. J Nucl Med. 2015;56:955–61.  https://doi.org/10.2967/jnumed.115.158402.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Makris NE, Huisman MC, Kinahan PE, Lammertsma AA, Boellaard R. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures. Eur J Nucl Med Mol Imaging. 2013;40:1507–15.  https://doi.org/10.1007/s00259-013-2465-0.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lasnon C, Quak E, Le Roux PY, Robin P, Hofman MS, Bourhis D, et al. EORTC PET response criteria are more influenced by reconstruction inconsistencies than PERCIST but both benefit from the EARL harmonization program. EJNMMI Phys. 2017;4:17.  https://doi.org/10.1186/s40658-017-0185-4.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Quak E, Le Roux PY, Lasnon C, Robin P, Hofman MS, Bourhis D, et al. Does PET SUV harmonization affect PERCIST response classification? J Nucl Med. 2016;57:1699–706.  https://doi.org/10.2967/jnumed.115.171983.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Berghmans T, Dusart M, Paesmans M, Hossein-Foucher C, Buvat I, Castaigne C, et al. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol. 2008;3:6–12.  https://doi.org/10.1097/JTO.0b013e31815e6d6b.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Lv Z, Fan J, Xu J, Wu F, Huang Q, Guo M, et al. Value of (18)F-FDG PET/CT for predicting EGFR mutations and positive ALK expression in patients with non-small cell lung cancer: a retrospective analysis of 849 Chinese patients. Eur J Nucl Med Mol Imaging. 2017;45(5):735–750  https://doi.org/10.1007/s00259-017-3885-z.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wong CY, Nunez R, Bohdiewicz P, Welsh RJ, Chmielewski GW, Ravikrishnan KP, et al. Patterns of abnormal FDG uptake by various histological types of non-small cell lung cancer at initial staging by PET. Eur J Nucl Med. 2001;28:1702–5.  https://doi.org/10.1007/s002590100638.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Yoshida T, Tanaka H, Kuroda H, Shimizu J, Horio Y, Sakao Y, et al. Standardized uptake value on (18)F-FDG-PET/CT is a predictor of EGFR T790M mutation status in patients with acquired resistance to EGFR-TKIs. Lung Cancer. 2016;100:14–19.  https://doi.org/10.1016/j.lungcan.2016.07.022.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 2009;50(Suppl 1):11S–20S.  https://doi.org/10.2967/jnumed.108.057182.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Boellaard R, O’Doherty MJ, Weber WA, Mottaghy FM, Lonsdale MN, Stroobants SG, et al. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. Eur J Nucl Med Mol Imaging. 2010;37:181–200.  https://doi.org/10.1007/s00259-009-1297-4.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Kaalep A, Sera T, Rijnsdorp S, Yaqub M, Talsma A, Lodge MA, et al. Feasibility of state of the art PET/CT systems performance harmonisation. Eur J Nucl Med Mol Imaging. 2018;45(8):1344–1361  https://doi.org/10.1007/s00259-018-3977-4.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Salavati A, Duan F, Snyder BS, Wei B, Houshmand S, Khiewvan B, et al. Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial. Eur J Nucl Med Mol Imaging. 2017;44:1969–83.  https://doi.org/10.1007/s00259-017-3753-x.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Soussan M, Chouahnia K, Maisonobe JA, Boubaya M, Eder V, Morere JF, et al. Prognostic implications of volume-based measurements on FDG PET/CT in stage III non-small-cell lung cancer after induction chemotherapy. Eur J Nucl Med Mol Imaging. 2013;40:668–76.  https://doi.org/10.1007/s00259-012-2321-7.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Lasnon C, Enilorac B, Popotte H, Aide N. Impact of the EARL harmonization program on automatic delineation of metabolic active tumour volumes (MATVs). EJNMMI Res. 2017;7:30.  https://doi.org/10.1186/s13550-017-0279-y.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nuclear Medicine DepartmentUniversity HospitalCaenFrance
  2. 2.Nuclear Medicine DepartmentFrançois Baclesse Cancer CentreCaenFrance
  3. 3.INSERM ANTICIPENormandie UniversityCaenFrance
  4. 4.Clinical Research DepartmentFrançois Baclesse Cancer CentreCaenFrance
  5. 5.Medical Informatics DepartmentFrançois Baclesse Cancer CentreCaenFrance
  6. 6.Lung Cancer UnitFrançois Baclesse Cancer CentreCaenFrance
  7. 7.Regional Cancer RegistryFrançois Baclesse Cancer CentreCaenFrance

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