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
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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.
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.
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.
KeywordsNon-small-cell lung cancer Survival FDG PET/CT Prognosticator Harmonization EARL accreditation program
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.
None to declare.
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.
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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
- 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.CrossRefPubMedGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedGoogle Scholar
- 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
- 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.CrossRefPubMedGoogle Scholar
- 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.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.CrossRefPubMedGoogle Scholar