Advertisement

European Radiology

, Volume 28, Issue 6, pp 2397–2405 | Cite as

Proton density fat fraction (PDFF) MRI for differentiation of benign and malignant vertebral lesions

  • Frederic Carsten Schmeel
  • Julian Alexander Luetkens
  • Peter Johannes Wagenhäuser
  • Michael Meier-Schroers
  • Daniel Lloyd Kuetting
  • Andreas Feißt
  • Jürgen Gieseke
  • Leonard Christopher Schmeel
  • Frank Träber
  • Hans Heinz Schild
  • Guido Matthias Kukuk
Magnetic Resonance

Abstract

Objectives

To investigate whether proton density fat fraction (PDFF) measurements using a six-echo modified Dixon sequence can help to differentiate between benign and malignant vertebral bone marrow lesions.

Methods

Sixty-six patients were prospectively enrolled in our study. In addition to conventional MRI at 3.0-Tesla including at least sagittal T2-weighted/spectral attenuated inversion recovery and T1-weighted sequences, all patients underwent a sagittal six-echo modified Dixon sequence of the spine. The mean PDFF was calculated using regions of interest and compared between vertebral lesions. A cut-off value of 6.40% in PDFF was determined by receiver operating characteristic curves and used to differentiate between malignant (< 6.40%) and benign (≥ 6.40%) vertebral lesions.

Results

There were 77 benign and 44 malignant lesions. The PDFF of malignant lesions was statistically significant lower in comparison with benign lesions (p < 0.001) and normal vertebral bone marrow (p < 0.001). The areas under the curves (AUC) were 0.97 for differentiating benign from malignant lesions (p < 0.001) and 0.95 for differentiating acute vertebral fractures from malignant lesions (p < 0.001). This yielded a diagnostic accuracy of 96% in the differentiation of both benign lesions and acute vertebral fractures from malignancy.

Conclusion

PDFF derived from six-echo modified Dixon allows for differentiation between benign and malignant vertebral lesions with a high diagnostic accuracy.

Key Points

Establishing a diagnosis of indeterminate vertebral lesions is a common clinical problem

Benign bone marrow processes may mimic the signal alterations observed in malignancy

PDFF differentiates between benign and malignant lesions with a high diagnostic accuracy

PDFF of non-neoplastic vertebral lesions is significantly higher than that of malignancy

PDFF from six-echo modified Dixon may help avoid potentially harmful bone biopsy

Keywords

Proton density fat fraction Modified Dixon method Chemical shift encoded imaging MRI Bone marrow malignancy 

Abbreviations and acronyms

AUC

Area Under the Curve

DWI

Diffusion-Weighted Imaging

mDixon

Modified Dixon

6E-mDixon

Six-Echo Modified Dixon

NPV

Negative Predictive Value

PDFF

Proton Density Fat Fraction

PET/CT

Positron Emission Tomography/Computed Tomography

PPV

Positive Predictive Value

ROC

Receiver Operating Characteristic

ROI

Region Of Interest

SE

Spin Echo

SENSE

Sensitivity Encoding

SPAIR

Spectral Attenuated Inversion Recovery

STIR

Short-Tau Inversion Recovery

TE

Echo Time

TR

Repetition Time

Notes

Funding

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

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Priv.-Doz. Dr. med. Guido Matthias Kukuk at Bonn University Hospital.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Dr. Jürgen Gieseke is an employee of Philips Healthcare (Best, The Netherlands) but had no control of inclusion of any data or data analysis. The other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

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

Ethical approval

Institutional review board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at one institution

References

  1. 1.
    Vogler JB 3rd, Murphy WA (1988) Bone marrow imaging. Radiology 168:679–693CrossRefPubMedGoogle Scholar
  2. 2.
    Ricci C, Cova M, Kang YS et al (1990) Normal age-related patterns of cellular and fatty bone marrow distribution in the axial skeleton: MR imaging study. Radiology 177:83–88CrossRefPubMedGoogle Scholar
  3. 3.
    Yuh WT, Zachar CK, Barloon TJ, Sato Y, Sickels WJ, Hawes DR (1989) Vertebral compression fractures: distinction between benign and malignant causes with MR imaging. Radiology 172:215–218CrossRefPubMedGoogle Scholar
  4. 4.
    Hanna SL, Fletcher BD, Fairclough DL, Jenkins JH 3rd, Le AH (1991) Magnetic resonance imaging of disseminated bone marrow disease in patients treated for malignancy. Skeletal Radiol 20:79–84CrossRefPubMedGoogle Scholar
  5. 5.
    Zajick DC Jr, Morrison WB, Schweitzer ME, Parellada JA, Carrino JA (2005) Benign and malignant processes: normal values and differentiation with chemical shift MR imaging in vertebral marrow. Radiology 237:590–596CrossRefPubMedGoogle Scholar
  6. 6.
    Douis H, Davies AM, Jeys L, Sian P (2016) Chemical shift MRI can aid in the diagnosis of indeterminate skeletal lesions of the spine. Eur Radiol 26:932–940CrossRefPubMedGoogle Scholar
  7. 7.
    Disler DG, McCauley TR, Ratner LM, Kesack CD, Cooper JA (1997) In-phase and out-of-phase MR imaging of bone marrow: prediction of neoplasia based on the detection of coexistent fat and water. AJR Am J Roentgenol 169:1439–1447CrossRefPubMedGoogle Scholar
  8. 8.
    Zampa V, Cosottini M, Michelassi C, Ortori S, Bruschini L, Bartolozzi C (2002) Value of opposed-phase gradient-echo technique in distinguishing between benign and malignant vertebral lesions. Eur Radiol 12:1811–1818CrossRefPubMedGoogle Scholar
  9. 9.
    Eggers H, Brendel B, Duijndam A, Herigault G (2011) Dual-echo Dixon imaging with flexible choice of echo times. Magn Reson Med 65:96–107CrossRefPubMedGoogle Scholar
  10. 10.
    Yoo YH, Kim HS, Lee YH et al (2015) Comparison of multi-echo Dixon methods with volume interpolated breath-hold gradient echo magnetic resonance imaging in fat-signal fraction quantification of paravertebral muscle. Korean J Radiol 16:1086–1095CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Karampinos DC, Ruschke S, Dieckmeyer M et al (2015) Modeling of T2* decay in vertebral bone marrow fat quantification. NMR Biomed 28:1535–1542CrossRefPubMedGoogle Scholar
  12. 12.
    Karampinos DC, Yu H, Shimakawa A, Link TM, Majumdar S (2011) T(1)-corrected fat quantification using chemical shift-based water/fat separation: application to skeletal muscle. Magn Reson Med 66:1312–1326CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Ren J, Dimitrov I, Sherry AD, Malloy CR (2008) Composition of adipose tissue and marrow fat in humans by 1H NMR at 7 Tesla. J Lipid Res 49:2055–2062CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kukuk GM, Hittatiya K, Sprinkart AM et al (2015) Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis. Eur Radiol 25:2869–2879CrossRefPubMedGoogle Scholar
  15. 15.
    Bray TJP, Bainbridge A, Punwani S, Ioannou Y, Hall-Craggs MA (2017) Simultaneous quantification of bone edema/adiposity and structure in inflamed bone using chemical shift-encoded MRI in spondyloarthritis. Magn Reson Med.  https://doi.org/10.1002/mrm.26729
  16. 16.
    Yoo HJ, Hong SH, Kim DH et al (2017) Measurement of fat content in vertebral marrow using a modified dixon sequence to differentiate benign from malignant processes. J Magn Reson Imaging 45:1534–1544CrossRefPubMedGoogle Scholar
  17. 17.
    Serai SD, Dillman JR, Trout AT (2017) Proton density fat fraction measurements at 1.5- and 3-T hepatic MR imaging: same-day agreement among readers and across two imager manufacturers. Radiology 284:244–254CrossRefPubMedGoogle Scholar
  18. 18.
    Yokoo T, Serai SD, Pirasteh A et al (2017) Linearity, bias, and precision of hepatic proton density fat fraction measurements by using MR imaging: a meta-analysis. Radiology.  https://doi.org/10.1148/radiol.2017170550:170550
  19. 19.
    Modic MT, Steinberg PM, Ross JS, Masaryk TJ, Carter JR (1988) Degenerative disk disease: assessment of changes in vertebral body marrow with MR imaging. Radiology 166:193–199CrossRefPubMedGoogle Scholar
  20. 20.
    Reeder SB, Sirlin CB (2010) Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin N Am 18:337–357 ixCrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Reeder SB, Hines CD, Yu H, McKenzie C, Brittain JH (2009) On the definition of fat-fraction for in vivo fat quantification with magnetic resonance imaging. Proc Int Soc Magn Reson Med 17:211Google Scholar
  22. 22.
    Bolan PJ, Arentsen L, Sueblinvong T et al (2013) Water-fat MRI for assessing changes in bone marrow composition due to radiation and chemotherapy in gynecologic cancer patients. J Magn Reson Imaging 38:1578–1584CrossRefPubMedGoogle Scholar
  23. 23.
    Myrehaug S, Sahgal A, Hayashi M et al (2017) Reirradiation spine stereotactic body radiation therapy for spinal metastases: systematic review. J Neurosurg Spine 27:428–435CrossRefPubMedGoogle Scholar
  24. 24.
    Karampinos DC, Melkus G, Baum T, Bauer JS, Rummeny EJ, Krug R (2014) Bone marrow fat quantification in the presence of trabecular bone: initial comparison between water-fat imaging and single-voxel MRS. Magn Reson Med 71:1158–1165CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Hines CD, Yu H, Shimakawa A, McKenzie CA, Brittain JH, Reeder SB (2009) T1 independent, T2* corrected MRI with accurate spectral modeling for quantification of fat: validation in a fat-water-SPIO phantom. J Magn Reson Imaging 30:1215–1222CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB (2008) Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 60:1122–1134CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Gee CS, Nguyen JT, Marquez CJ et al (2015) Validation of bone marrow fat quantification in the presence of trabecular bone using MRI. J Magn Reson Imaging 42:539–544CrossRefPubMedGoogle Scholar
  28. 28.
    Arentsen L, Yagi M, Takahashi Y et al (2015) Validation of marrow fat assessment using noninvasive imaging with histologic examination of human bone samples. Bone 72:118–122CrossRefPubMedGoogle Scholar
  29. 29.
    Tang A, Desai A, Hamilton G et al (2015) Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease. Radiology 274:416–425CrossRefPubMedGoogle Scholar
  30. 30.
    Padhani AR, van Ree K, Collins DJ, D'Sa S, Makris A (2013) Assessing the relation between bone marrow signal intensity and apparent diffusion coefficient in diffusion-weighted MRI. AJR Am J Roentgenol 200:163–170CrossRefPubMedGoogle Scholar
  31. 31.
    Hayashida Y, Hirai T, Yakushiji T et al (2006) Evaluation of diffusion-weighted imaging for the differential diagnosis of poorly contrast-enhanced and T2-prolonged bone masses: Initial experience. J Magn Reson Imaging 23:377–382CrossRefPubMedGoogle Scholar
  32. 32.
    Raya JG, Dietrich O, Reiser MF, Baur-Melnyk A (2005) Techniques for diffusion-weighted imaging of bone marrow. Eur J Radiol 55:64–73CrossRefPubMedGoogle Scholar
  33. 33.
    Hacklander T, Scharwachter C, Golz R, Mertens H (2006) [Value of diffusion-weighted imaging for diagnosing vertebral metastases due to prostate cancer in comparison to other primary tumors]. Rofo 178:416–424CrossRefPubMedGoogle Scholar
  34. 34.
    Latifoltojar A, Hall-Craggs M, Bainbridge A et al (2017) Whole-body MRI quantitative biomarkers are associated significantly with treatment response in patients with newly diagnosed symptomatic multiple myeloma following bortezomib induction. Eur Radiol.  https://doi.org/10.1007/s00330-017-4907-8
  35. 35.
    Takasu M, Kaichi Y, Tani C et al (2015) Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) magnetic resonance imaging as a biomarker for symptomatic multiple myeloma. PLoS One 10:e0116842CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Takasu M, Tani C, Sakoda Y et al (2012) Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) imaging of multiple myeloma: initial clinical efficiency results. Eur Radiol 22:1114–1121CrossRefPubMedGoogle Scholar
  37. 37.
    Kugel H, Jung C, Schulte O, Heindel W (2001) Age- and sex-specific differences in the 1H-spectrum of vertebral bone marrow. J Magn Reson Imaging 13:263–268CrossRefPubMedGoogle Scholar
  38. 38.
    Griffith JF, Yeung DK, Ma HT, Leung JC, Kwok TC, Leung PC (2012) Bone marrow fat content in the elderly: a reversal of sex difference seen in younger subjects. J Magn Reson Imaging 36:225–230CrossRefPubMedGoogle Scholar
  39. 39.
    Martin J, Nicholson G, Cowin G, Ilente C, Wong W, Kennedy D (2014) Rapid determination of vertebral fat fraction over a large range of vertebral bodies. J Med Imaging Radiat Oncol 58:155–163CrossRefPubMedGoogle Scholar
  40. 40.
    Baum T, Yap SP, Dieckmeyer M et al (2015) Assessment of whole spine vertebral bone marrow fat using chemical shift-encoding based water-fat MRI. J Magn Reson Imaging 42:1018–1023CrossRefPubMedGoogle Scholar
  41. 41.
    Wendt RE 3rd, Wilcott MR 3rd, Nitz W, Murphy PH, Bryan RN (1988) MR imaging of susceptibility-induced magnetic field inhomogeneities. Radiology 168:837–841CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Frederic Carsten Schmeel
    • 1
  • Julian Alexander Luetkens
    • 1
  • Peter Johannes Wagenhäuser
    • 1
  • Michael Meier-Schroers
    • 1
  • Daniel Lloyd Kuetting
    • 1
  • Andreas Feißt
    • 1
  • Jürgen Gieseke
    • 2
  • Leonard Christopher Schmeel
    • 1
  • Frank Träber
    • 1
  • Hans Heinz Schild
    • 1
  • Guido Matthias Kukuk
    • 1
  1. 1.Department of Radiology and Radiation OncologyUniversity Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität BonnBonnGermany
  2. 2.Philips HealthcareBestThe Netherlands

Personalised recommendations