Advertisement

Comparison of 18F-Choline PET/CT and MRI functional parameters in prostate cancer

  • Xavier Palard-Novello
  • Luc Beuzit
  • Giulio Gambarota
  • Florence Le Jeune
  • Etienne Garin
  • Pierre-Yves Salaün
  • Anne Devillers
  • Solène Querellou
  • Patrick Bourguet
  • Hervé Saint-Jalmes
Original Article
  • 18 Downloads

Abstract

Aim

18F-Choline (FCH) uptake parameters are strong indicators of aggressive disease in prostate cancer. Functional parameters derived by magnetic resonance imaging (MRI) are also correlated to aggressive disease. The aim of this work was to evaluate the relationship between metabolic parameters derived by FCH PET/CT and functional parameters derived by MRI.

Materials and methods

Fourteen patients with proven prostate cancer who underwent FCH PET/CT and multiparametric MRI were enrolled. FCH PET/CT consisted in a dual phase: early pelvic list-mode acquisition and late whole-body acquisition. FCH PET/CT and multiparametric MRI examinations were registered and tumoral volume-of-interest were drawn on the largest lesion visualized on the apparent diffusion coefficient (ADC) map and projected onto the different multiparametric MR images and FCH PET/CT images. Concerning the FCH uptake, kinetic parameters were extracted with the best model selected using the Akaike information criterion between the one- and two-tissue compartment models with an imaging-derived plasma input function. Other FCH uptake parameters (early SUVmean and late SUVmean) were extracted. Concerning functional parameters derived by MRI scan, cell density (ADC from diffusion weighting imaging) and vessel permeability (Ktrans and Ve using the Tofts pharmakinetic model from dynamic contrast-enhanced imaging) parameters were extracted. Spearman’s correlation coefficients were calculated to compare parameters.

Results

The one-tissue compartment model for kinetic analysis of PET images was selected. Concerning correlation analysis between PET parameters, K1 was highly correlated with early SUVmean (r = 0.83, p < 0.001) and moderately correlated with late SUVmean (r = 0.66, p = 0.010) and early SUVmean was highly correlated with late SUVmean (r = 0.90, p < 0.001). No significant correlation was found between functional MRI parameters. Concerning correlation analysis between PET and functional MRI parameters, K1 (from FCH PET/CT imaging) was moderately correlated with Ktrans (from perfusion MR imaging) (r = 0.55, p = 0.041).

Conclusions

No significant correlation was found between FCH PET/CT and multiparametric MRI metrics except FCH influx which is moderately linked to the vessel permeability in prostate cancer.

Keywords

18F-Choline Positron emission tomography Prostate cancer Kinetic analysis Multiparametric MRI 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

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.

Informed consent

For this type of study (retrospective), the local ethics committee waived the requirement for informed consent.

References

  1. 1.
    Zhou CK, Check DP, Lortet-Tieulent J, Laversanne M, Jemal A, Ferlay J, et al. Prostate cancer incidence in 43 populations worldwide: an analysis of time trends overall and by age group. Int J Cancer. 2016;138(6):1388–400.CrossRefGoogle Scholar
  2. 2.
    Schaefferkoetter JD, Wang Z, Stephenson MC, Roy S, Conti M, Eriksson L, et al. Quantitative 18F-fluorocholine positron emission tomography for prostate cancer: correlation between kinetic parameters and Gleason scoring. EJNMMI Res. 2017;7(1):25.CrossRefGoogle Scholar
  3. 3.
    Palard-Novello X, Blin AL, Bourhis D, Garin E, Salaun PY, Devillers A, et al. Comparison of choline influx from dynamic (18)F-Choline PET/CT and clinicopathological parameters in prostate cancer initial assessment. Ann Nucl Med. 2018;32(4):281–87.CrossRefGoogle Scholar
  4. 4.
    Tamada T, Prabhu V, Li J, Babb JS, Taneja SS, Rosenkrantz AB. Prostate cancer: diffusion-weighted MR imaging for detection and assessment of aggressiveness-comparison between conventional and kurtosis models. Radiology. 2017;284(1):100–8.CrossRefGoogle Scholar
  5. 5.
    Peng Y, Jiang Y, Yang C, Brown JB, Antic T, Sethi I, et al. Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score—a computer-aided diagnosis development study. Radiology. 2013;267(3):787–96.CrossRefGoogle Scholar
  6. 6.
    Oto A, Yang C, Kayhan A, Tretiakova M, Antic T, Schmid-Tannwald C, et al. Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. AJR Am J Roentgenol. 2011;197(6):1382–90.CrossRefGoogle Scholar
  7. 7.
    Hauth E, Halbritter D, Jaeger H, Hohmuth H, Beer M. Diagnostic value of semi-quantitative and quantitative analysis of functional parameters in multiparametric MRI of the prostate. Br J Radiol. 2017;90(1078):20170067.CrossRefGoogle Scholar
  8. 8.
    Vos EK, Litjens GJ, Kobus T, Hambrock T, Hulsbergen-van de Kaa CA, Barentsz JO, et al. Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T. Eur Urol. 2013;64(3):448–55.CrossRefGoogle Scholar
  9. 9.
    Hotker AM, Mazaheri Y, Aras O, Zheng J, Moskowitz CS, Gondo T, et al. Assessment of prostate cancer aggressiveness by use of the combination of quantitative DWI and dynamic contrast-enhanced MRI. AJR Am J Roentgenol. 2016;206(4):756–63.CrossRefGoogle Scholar
  10. 10.
    Piert M, Montgomery J, Kunju LP, Siddiqui J, Rogers V, Rajendiran T, et al. 18F-Choline PET/MRI: the additional value of PET for MRI-guided transrectal prostate biopsies. J Nucl Med Off Publ Soc Nucl Med. 2016;57(7):1065–70.Google Scholar
  11. 11.
    Pinkawa M, Piroth MD, Holy R, Klotz J, Djukic V, Corral NE, et al. Dose-escalation using intensity-modulated radiotherapy for prostate cancer—evaluation of quality of life with and without (18)F-choline PET-CT detected simultaneous integrated boost. Radiat Oncol. 2012;7:14.CrossRefGoogle Scholar
  12. 12.
    DeGrado TR, Baldwin SW, Wang S, Orr MD, Liao RP, Friedman HS, et al. Synthesis and evaluation of (18)F-labeled choline analogs as oncologic PET tracers. J Nucl Med Off Publ Soc Nucl Med. 2001;42(12):1805–14.Google Scholar
  13. 13.
    DeGrado TR, Reiman RE, Price DT, Wang S, Coleman RE. Pharmacokinetics and radiation dosimetry of 18F-fluorocholine. J Nucl Med Off Publ Soc Nucl Med. 2002;43(1):92–6.Google Scholar
  14. 14.
    Jadvar H. Prostate cancer: PET with 18F-FDG, 18F- or 11C-acetate, and 18F- or 11C-choline. Journal of nuclear medicine: official publication. Soc Nucl Med. 2011;52(1):81–9.CrossRefGoogle Scholar
  15. 15.
    Bhakoo KK, Williams SR, Florian CL, Land H, Noble MD. Immortalization and transformation are associated with specific alterations in choline metabolism. Cancer Res. 1996;56(20):4630–5.PubMedGoogle Scholar
  16. 16.
    Massaro A, Ferretti A, Secchiero C, Cittadin S, Milan E, Tamiso L, et al. Optimising 18F-choline PET/CT acquisition protocol in prostate cancer patients. N Am J Med Sci. 2012;4:416–20.CrossRefGoogle Scholar
  17. 17.
    Chondrogiannis S, Marzola MC, Grassetto G, Maffione AM, Rampin L, Veronese E, et al. New acquisition protocol of 18F-choline PET/CT in prostate cancer patients: review of the literature about methodology and proposal of standardization. Biomed Res Int. 2014;2014:215650.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Palard-Novello X, Blin AL, Le Jeune F, Garin E, Salaun PY, Devillers A, et al. Optimization of temporal sampling for (18)F-choline uptake quantification in prostate cancer assessment. EJNMMI Res. 2018;8(1):49.CrossRefGoogle Scholar
  19. 19.
    Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging JMRI. 1999;10(3):223–32.CrossRefGoogle Scholar
  20. 20.
    Glatting G, Kletting P, Reske SN, Hohl K, Ring C. Choosing the optimal fit function: comparison of the Akaike information criterion and the F-test. Med Phys. 2007;34(11):4285–92.CrossRefGoogle Scholar
  21. 21.
    Plathow C, Weber WA. Tumor cell metabolism imaging. J Nucl Med Off Publ Soc Nucl Med. 2008;49(Suppl 2):43S–63S.Google Scholar
  22. 22.
    Michel V, Yuan Z, Ramsubir S, Bakovic M. Choline transport for phospholipid synthesis. Exp Biol Med. 2006;231(5):490–504.CrossRefGoogle Scholar
  23. 23.
    de Perrot T, Rager O, Scheffler M, Lord M, Pusztaszeri M, Iselin C, et al. Potential of hybrid (1)(8)F-fluorocholine PET/MRI for prostate cancer imaging. Eur J Nucl Med Mol Imaging. 2014;41(9):1744–55.CrossRefGoogle Scholar
  24. 24.
    Rakheja R, Chandarana H, DeMello L, Jackson K, Geppert C, Faul D, et al. Correlation between standardized uptake value and apparent diffusion coefficient of neoplastic lesions evaluated with whole-body simultaneous hybrid PET/MRI. AJR American J Roentgenol. 2013;201(5):1115–9.CrossRefGoogle Scholar
  25. 25.
    Heusch P, Buchbender C, Kohler J, Nensa F, Beiderwellen K, Kuhl H, et al. Correlation of the apparent diffusion coefficient (ADC) with the standardized uptake value (SUV) in hybrid 18F-FDG PET/MRI in non-small cell lung cancer (NSCLC) lesions: initial results. RoFo: Fortschritte auf dem Gebiete der Rontgenstrahlen der Nuklearmedizin. 2013;185(11):1056–62.CrossRefGoogle Scholar
  26. 26.
    Byun BH, Kong CB, Lim I, Choi CW, Song WS, Cho WH, et al. Combination of 18F-FDG PET/CT and diffusion-weighted MR imaging as a predictor of histologic response to neoadjuvant chemotherapy: preliminary results in osteosarcoma. J Nucl Med Off Publ Soc Nucl Med. 2013;54(7):1053–9.Google Scholar
  27. 27.
    Vadi SK, Singh B, Basher RK, Watts A, Sood AK, Lal A, et al. 18F-fluorocholine PET/CT complementing the role of dynamic contrast-enhanced MRI for providing comprehensive diagnostic workup in prostate cancer patients with suspected relapse following radical prostatectomy. Clin Nucl Med. 2017;42(8):e355-e36.Google Scholar
  28. 28.
    Metser U, Berlin A, Halankar J, Murphy G, Jhaveri KS, Ghai S, et al. 18F-fluorocholine PET whole-body MRI in the staging of high-risk prostate cancer. AJR Am J Roentgenol. 2018;210(3):635–40.CrossRefGoogle Scholar
  29. 29.
    Verwer EE, Oprea-Lager DE, van den Eertwegh AJ, van Moorselaar RJ, Windhorst AD, Schwarte LA, et al. Quantification of 18F-fluorocholine kinetics in patients with prostate cancer. J Nucl Med Off Publ Soc Nucl Med. 2015;56(3):365–71.Google Scholar
  30. 30.
    Choi JY, Yang J, Noworolski SM, Behr S, Chang AJ, Simko JP, et al. 18f fluorocholine dynamic time-of-flight PET/MR imaging in patients with newly diagnosed intermediate- to high-risk prostate cancer: initial clinical-pathologic comparisons. Radiology. 2017;282(2):429–36.CrossRefGoogle Scholar
  31. 31.
    Grkovski M, Gharzeddine K, Sawan P, Schoder H, Michaud L, Weber WA, et al. 11C-choline pharmacokinetics in recurrent prostate cancer. J Nucl Med. 2018.  https://doi.org/10.2967/jnumed.118.210088.CrossRefPubMedGoogle Scholar
  32. 32.
    Takesh M. Kinetic modeling application to (18)F-fluoroethylcholine positron emission tomography in patients with primary and recurrent prostate cancer using two-tissue compartmental model. World J Nucl Med. 2013;12(3):101–10.CrossRefGoogle Scholar
  33. 33.
    Iorio E, Mezzanzanica D, Alberti P, Spadaro F, Ramoni C, D’Ascenzo S, et al. Alterations of choline phospholipid metabolism in ovarian tumor progression. Cancer Res. 2005;65(20):9369–76.CrossRefGoogle Scholar
  34. 34.
    Cho E, Chung DJ, Yeo DM, Sohn D, Son Y, Kim T, et al. Optimal cut-off value of perfusion parameters for diagnosing prostate cancer and for assessing aggressiveness associated with Gleason score. Clin Imaging. 2015;39(5):834–40.CrossRefGoogle Scholar
  35. 35.
    Xiao H, Tan F, Goovaerts P, Adunlin G, Ali A, Huang Y, et al. Factors associated with time-to-treatment of prostate cancer in Florida. J Health Care Poor Underserved. 2013;24(4 Suppl):132–46.PubMedPubMedCentralGoogle Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2018

Authors and Affiliations

  • Xavier Palard-Novello
    • 1
    • 2
  • Luc Beuzit
    • 3
  • Giulio Gambarota
    • 1
  • Florence Le Jeune
    • 2
    • 4
  • Etienne Garin
    • 2
    • 5
  • Pierre-Yves Salaün
    • 6
    • 7
  • Anne Devillers
    • 2
  • Solène Querellou
    • 6
    • 7
  • Patrick Bourguet
    • 2
  • Hervé Saint-Jalmes
    • 1
    • 2
  1. 1.Univ Rennes, Inserm, LTSI-UMR1099RennesFrance
  2. 2.Department of Nuclear MedicineCentre Eugène MarquisRennesFrance
  3. 3.Department of Medical ImagingCentre Hospitalier UniversitaireRennesFrance
  4. 4.Univ Rennes-EA 4712RennesFrance
  5. 5.Univ Rennes, Inserm, UMR 124RennesFrance
  6. 6.Department of Nuclear MedicineCentre Hospitalier UniversitaireBrestFrance
  7. 7.University of Bretagne Occidentale, EA 3878BrestFrance

Personalised recommendations