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Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas

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Abstract

Objectives

To compare dynamic contrast-enhanced MRI (DCE-MRI) data obtained using different prebolus T1 values in glioma grading and molecular profiling.

Methods

We retrospectively reviewed 83 cases of gliomas: 46 lower-grade gliomas (LGG; grades II and III) and 37 high-grade gliomas (HGG; grade IV). DCE-MRI maps of plasma volume fraction (Vp), extravascular-extracellular volume fraction (Ve), and tracer transfer constant from plasma to tissue (Ktrans) were obtained using a fixed T1 value of 1400 ms and a measured T1 obtained with variable flip angle (VFA). Tumour segmentations were performed and first-order histogram parameters were extracted from volumes of interest (VOIs) after co-registration with the perfusion maps. The two methods were compared using Wilcoxon matched-pairs signed-rank test and Bland-Altman analysis. Diagnostic accuracy was obtained and compared using ROC curve analysis and DeLong’s test.

Results

Perfusion parameters obtained with the fixed T1 value were significantly higher than those obtained with the VFA. As regards diagnostic accuracy, there were no significant differences between the two methods both for glioma grading and molecular classification, except for few parameters of both methods.

Conclusions

DCE-MRI data obtained with different prebolus T1 are not comparable and the definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI.

Key Points

• DCE-MRI data obtained with different prebolus T1 are significantly different, thus not comparable.

• The definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI for glioma grading.

• The use of a fixed T1 value represents a valid alternative to T1 mapping for DCE-MRI analysis.

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Abbreviations

HGG:

High-grade gliomas

IDH:

Isocitrate dehydrogenase

K trans :

Tracer transfer constant from plasma to tissue

LGG:

Lower-grade gliomas

Ve:

Extravascular-extracellular volume fraction

VFA:

Variable flip angle

Vp:

Plasma volume fraction

References

  1. Anzalone N, Castellano A, Cadioli M et al (2018) Brain gliomas: multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images. Radiology 287(3):933–943. https://doi.org/10.1148/radiol.2017170362

    Article  PubMed  Google Scholar 

  2. Nguyen TB, Cron GO, Perdrizet K et al (2015) Comparison of the diagnostic accuracy of DSC- and dynamic contrastenhanced MRI in the preoperative grading of astrocytomas. AJNR Am J Neuroradiol 36:2017–2022. https://doi.org/10.3174/ajnr.A4398

  3. Taunk NK, Oh JH, Shukla-Dave A et al (2017) Early post-treatment assessment of MRI perfusion biomarkers can predict long-term response of lung cancer brain metastases to stereotactic radiosurgery. Neuro Oncol 20(4):567–575. https://doi.org/10.1093/neuonc/nox159

    Article  CAS  PubMed Central  Google Scholar 

  4. Abe T, Mizobuchi Y, Nakajima K et al (2015) Diagnosis of brain tumors using dynamic contrast-enhanced perfusion imaging with a short acquisition time. Springerplus 4:1–6. https://doi.org/10.1186/s40064-015-0861-6

    Article  Google Scholar 

  5. Ulyte A, Katsaros VK, Liouta E et al (2016) Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 58:1197–1208. https://doi.org/10.1007/s00234-016-1741-7

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yoo RE, Choi SH, Kim TM et al (2017) Dynamic contrast-enhanced MR imaging in predicting progression of enhancing lesions persisting after standard treatment in glioblastoma patients: a prospective study. Eur Radiol 27:3156–3166. https://doi.org/10.1007/s00330-016-4692-9

    Article  PubMed  Google Scholar 

  7. O'Connor JP, Tofts PS, Miles KA, Parkes LM, Thompson G, Jackson A (2011) Dynamic contrast-enhanced imaging techniques: CT and MRI. Br J Radiol 84 Spec No 2:S112–20. https://doi.org/10.1259/bjr/55166688

  8. Tietze A, Mouridsen K, Mikkelsen IK (2015) The impact of reliable prebolus T 1 measurements or a fixed T 1 value in the assessment of glioma patients with dynamic contrast enhancing MRI. Neuroradiology 57:561–572. https://doi.org/10.1007/s00234-015-1502-z

    Article  PubMed  Google Scholar 

  9. Heye AK, Thrippleton MJ, Armitage PA et al (2016) Tracer kinetic modelling for DCE-MRI quantification of subtle blood–brain barrier permeability. Neuroimage 125:446–455. https://doi.org/10.1016/j.neuroimage.2015.10.018

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ellingson BM, Bendszus M, Sorensen AG, Pope WB (2014) Emerging techniques and technologies in brain tumor imaging. Neuro Oncol 16:vii12–vii23. https://doi.org/10.1093/neuonc/nou221

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dale BM, Jesberger JA, Lewin JS, Hillenbrand CM, Duerk JL (2003) Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI. J Magn Reson Imaging 18:575–584. https://doi.org/10.1002/jmri.10399

  12. Conte GM, Castellano A, Altabella L et al (2017) Reproducibility of dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI in the study of brain gliomas: a comparison of data obtained using different commercial software. Radiol Med 122:294–302. https://doi.org/10.1007/s11547-016-0720-8

    Article  PubMed  Google Scholar 

  13. Jahng GH, Stables L, Ebel A et al (2005) Sensitive and fast T1 mapping based on two inversion recovery images and a reference image. Med Phys 32:1524–1528. https://doi.org/10.1118/1.1915014

    Article  PubMed  PubMed Central  Google Scholar 

  14. Henderson E, McKinnon G, Lee TY, Rutt BK (1999) A fast 3D Look-Locker method for volumetric T1 mapping. Magn Reson Imaging 17:1163–1171. https://doi.org/10.1016/S0730-725X(99)00025-9

    Article  CAS  PubMed  Google Scholar 

  15. Fram EK, Herfkens RJ, Johnson GA et al (1987) Rapid calculation of T1 using variable flip angle gradient refocused imaging. Magn Reson Imaging 5:201–208. https://doi.org/10.1016/0730-725X(87)90021-X

    Article  CAS  PubMed  Google Scholar 

  16. Chang L, Koay C, Basser PJ, Pierpaoli C (2008) Linear least-squares method for unbiased estimation of T1 from SPGR signals. Magn Reson Med 60:496–501. https://doi.org/10.1002/mrm.21669

    Article  PubMed  PubMed Central  Google Scholar 

  17. Stikov N, Boudreau M, Levesque IR, Tardif CL, Barral JK, Pike GB (2015) On the accuracy of T1 mapping: searching for common ground. Magn Reson Med 73:514–522. https://doi.org/10.1002/mrm.25135

  18. Larsson C, Kleppestø M, Grothe I, Vardal J, Bjørnerud A (2015) T1 in high-grade glioma and the influence of different measurement strategies on parameter estimations in DCE-MRI. J Magn Reson Imaging 42:97–104. https://doi.org/10.1002/jmri.24772

  19. Nam JG, Kang KM, Choi SH et al (2017) Comparison between the prebolus T1 measurement and the fixed T1 value in dynamic contrast-enhanced MR imaging for the differentiation of true progression from pseudoprogression in glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy. AJNR Am J Neuroradiol 38:2243–2250. https://doi.org/10.3174/ajnr.A5417

    Article  CAS  PubMed  Google Scholar 

  20. Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1

  21. Mouridsen K, Christensen S, Gyldensted L, Ostergaard L (2006) Automatic selection of arterial input function using cluster analysis. Magn Reson Med 55:524–531. https://doi.org/10.1002/mrm.20759

  22. Tofts PS, Brix G, Buckley DL et al (1999) Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223–232

    Article  CAS  Google Scholar 

  23. DCE MRI Technical Committee. DCE MRI Quantification Profile, Quantitative Imaging Biomarkers Alliance. Version 1.0. Reviewed Draft. QIBA, July 1, 2012. Available from: http://qibawiki.rsna.org/index.php/Profiles

  24. Sengupta A, Gupta R, Singh A (2017) Evaluation of B1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T. J Transl Med 15:242. https://doi.org/10.1186/s12967-017-1349-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank Marcello Cadioli and Antonella Iadanza for technical support, and Bradley J. Erickson for reviewing the article.

Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. A. Falini and N. Anzalone contributed equally to this work.

Authors and Affiliations

  1. Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy

    G. M. Conte, L. Altabella, A. Castellano, A. Falini & N. Anzalone

  2. Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy

    V. Cuccarini & A. Bizzi

  3. Department of Radiology, Humanitas Clinical and Research Hospital, Rozzano, Milan, Italy

    M. Grimaldi

  4. Department of Neuroradiology, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Milan, Italy

    A. Costa

  5. Department of Neuroscience and Imaging and ITAB-Institute of Advanced Biomedical Technologies, University G. D’Annunzio, Chieti, Italy

    M. Caulo

Authors
  1. G. M. Conte
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  2. L. Altabella
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  3. A. Castellano
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  6. M. Grimaldi
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  7. A. Costa
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  9. A. Falini
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  10. N. Anzalone
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Corresponding author

Correspondence to N. Anzalone.

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Guarantor

The scientific guarantor of this publication is Nicoletta Anzalone.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Nicoletta Anzalone is a member of the advisory board of Bracco; is a consultant for Bayer Healthcare; is on the speakers bureaus of Bayer Healthcare and Guerbet.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

All study subjects have been previously reported in: Anzalone N, Castellano A, Cadioli M, Conte GM, et al. Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images. Radiology, 2018.

Methodology

• retrospective

• diagnostic study

• multicentre study

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Conte, G.M., Altabella, L., Castellano, A. et al. Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas. Eur Radiol 29, 3467–3479 (2019). https://doi.org/10.1007/s00330-019-06122-x

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