Skip to main content
SpringerLink
Log in

Use of monoexponential diffusion-weighted imaging and diffusion kurtosis imaging and dynamic contrast-enhanced-MRI for the differentiation of spinal tumors

  • Original Article
  • Published:
European Spine Journal Aims and scope Submit manuscript

Abstract

Purpose

To explore the diagnostic value of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE)-MRI for differentiating between spinal malignant and non-malignant tumors lacking typical imaging signs and correlation between the parameters of the three models.

Methods

DWI, DKI, and DCE-MRI examinations were performed in 39 and 27 cases of spinal malignant and non-malignant tumors, respectively. Two radiologists independently evaluated apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) of the DWI and DKI models, and volume transfer constant (Ktrans), rate constant (kep), and extracellular extravascular volume ratio (ve) of the DCE-MRI model for post-processing analyses. Statistical differences of parameters were compared using an independent sample t test. The sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve were determined. Pearson correlation analysis was used to evaluate the correlation between these parameters.

Results

ADC, MD, and ve were significantly lower, while MK and kep were significantly higher for spinal malignant tumors than for non-malignant tumors. The MK had the highest area under the ROC curve of 0.940 and sensitivity (96.3%). Ve was weakly positively correlated with ADC (r = 0.468) and MD (r = 0.363) and weakly negatively correlated with MK (r = −0.469). kep was weakly positively correlated with MK (r = 0.375). Ktrans was weakly positively correlated with ADC (r = 0.325).

Conclusions

Monoexponential DWI, DKI, and DCE-MRI have potential value in the differentiation of spinal malignant from non-malignant tumors lacking typical imaging signs, and there is a certain correlation between the parameters of the three models.

Graphic abstract

These slides can be retrieved under Electronic Supplementary Material.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under curve

DCE-MRI:

Dynamic contrast-enhanced-MRI

DKI:

Diffusion kurtosis imaging

DWI:

Diffusion-weighted imaging

FA:

Flip angle

FOV:

Field of view

ICC:

Intraclass correlation coefficient

K trans :

Volume transfer constant

k ep :

Rate constant

MD:

Mean diffusivity

MK:

Mean kurtosis

ROC:

Receiver operating characteristic curve

ROI:

Region of interest

v e :

Extracellular extravascular volume ratio

References

  1. Zambo I, Veselý K (2014) WHO classification of tumours of soft tissue and bone 2013: the main changes compared to the 3rd edition. Cesk Patol 50(2):64–70

    PubMed  Google Scholar 

  2. Wang Fengxian, Chu Chen, Zhao Cheng et al (2019) Diffusion kurtosis imaging in sacroiliitis to evaluate the activity of ankylosing spondylitis. J Magn Reson Imaging 49(1):101–108

    Article  PubMed  Google Scholar 

  3. Hui ES, Cheung MM, Qi L et al (2008) Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neurimage 42(1):122–134

    Article  Google Scholar 

  4. Surov A, Nagata S, Razek AA, Tirumani SH, Wienke A, Kahn T (2015) Comparison of ADC values in different malignancies of the skeletal musculature: a multicentric analysis. Skelet Radiol 44(7):995–1000

    Article  Google Scholar 

  5. Ahlawat S, Khandheria P, Subhawong TK, Fayad LM (2015) Differentiation of benign and malignant skeletal lesions with quantitative diffusion weighted MRI at 3T. Eur J Radiol 84(6):1091–1097

    Article  PubMed  Google Scholar 

  6. Surov A, Behrmann C (2014) Diffusion-weighted imaging of skeletal muscle lymphoma. Skelet Radiol 43(7):899–903

    Article  Google Scholar 

  7. Jenson JH, Helpern JA, Ramani A et al (2005) Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53(6):1432–1440

    Article  Google Scholar 

  8. Bai Y, Lin YS, Tian J (2016) Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion weighted MR imaging and diffusion kurtosis MR imaging. Radiology 278(2):496–504

    Article  PubMed  Google Scholar 

  9. Zhu L, Pan ZL, Ma Q et al (2017) Diffusion kurtosis imaging study of rectal adenocarcinoma associated with histopathologic prognostic factors: preliminary findings. Radiology 284(1):66–76

    Article  PubMed  Google Scholar 

  10. Sun K, Chen X, Chai W et al (2015) Breast cancer: diffusion kurtosis MR imaging-diagnostic accuracy and correlation with clinical-pathologic factors. Radiology 277(1):46–55

    Article  PubMed  Google Scholar 

  11. Nogueira L, Brandao S, Matos E et al (2014) Application of the diffusion kurtosis model for the study of breast lesions. Eur Radiol 24(6):1197–1203

    Article  PubMed  Google Scholar 

  12. Ogawa Masaki, Kan Hirohito, Arai Nobuyuki et al (2019) Differentiation between malignant and benign musculoskeletal tumors using diffusion kurtosis imaging. Skelet Radiol 48(2):285–292

    Article  Google Scholar 

  13. Zheng D, Chen Y, Chen Y et al (2014) Dynamic contrast-enhanced MRI of nasopharyngeal carcinoma: a preliminary study of the correlations between quantitative parameters and clinical stage. J Magn Reson Imaging 39(4):940–948

    Article  PubMed  Google Scholar 

  14. Saha A, Peck KK, Lis E et al (2014) Magnetic resonance perfusion characteristics of hypervascular renal and hypovascular prostate spinal metastases: clinical utilities and implications. Spine 39(24):E1433–E1440

    Article  PubMed  PubMed Central  Google Scholar 

  15. Khadem NR, Karimi S, Peck KK et al (2012) Characterizing hypervascular and hypovascular metastases and normal bone marrow of the spine using dynamic contrast enhanced MR imaging. AJNR Am J Neuroradiol 33(11):2178–2185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lang N, Su MY, Yu HJ et al (2013) Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI. Magn Reson Imaging 31(8):1285–1291

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lang N, Su MY, Xing X et al (2017) Morphological and dynamic contrast enhanced MR imaging features for the differentiation of chordoma and giant cell tumors in the axial skeleton. J Magn Reson Imaging 45(4):1068–1075

    Article  PubMed  Google Scholar 

  18. Lang N, Su MY, Yu HJ et al (2015) Differentiation of tuberculosis and metastatic cancer in the spine using dynamic contrast-enhanced MRI. Eur Spine J 24(8):1729–1737

    Article  PubMed  Google Scholar 

  19. Tofts PS (1997) Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 7(1):91–101

    Article  CAS  PubMed  Google Scholar 

  20. Hatakenaka M, Soeda H, Yabuuchi H et al (2008) Apparent diffusion coefficients of breast tumors: clinical application. Magn Reson Med Sci 7(1):23–29

    Article  PubMed  Google Scholar 

  21. Hui ES, Cheung MM, Qi L et al (2008) Advanced MR diffusion characterization of neural tissue using directional diffusion kurtosis analysis. Conf Proc IEEE Eng Med Biol Soc 2008:3941–3944

    Google Scholar 

  22. Huang W, Chen YY, Fedorov A et al (2016) The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge. Tomography 2(1):56–66

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hu H, Xu XQ, Liu H et al (2017) Orbital benign and malignant lymphoproliferative disorders: differentiation using semi-quantitative and quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging. Eur J Radiol 88:88–94

    Article  PubMed  Google Scholar 

  24. Park M, Kim J, Choi YS et al (2016) Application of dynamic contrast-enhanced MRI parameters for differentiating squamous cell carcinoma and malignant lymphoma of the oropharynx. AJR Am J Roentgenol 206(2):401–407

    Article  PubMed  Google Scholar 

  25. Haradome K, Haradome H, Usui Y et al (2014) Orbital lymphoproliferative disorders (OLPDs): value of MR imaging for differentiating orbital lymphoma from benign OPLDs. AJNR Am J Neuroradiol 35(10):1976–1982

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li Ting, Tao Yu, Li Lyu et al (2018) Use of diffusion kurtosis imaging and quantitative dynamic contrast-enhanced MRI for the differentiation of breast tumors. J Magn Reson Imaging 48(5):1358–1366

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study has received funding by National Natural Science Foundation of China (81701648, 81971578) and Clinical key project of Peking University Third Hospital (BYSY2018007).

Author information

Authors and Affiliations

  1. Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People’s Republic of China

    Jiahui Zhang, Yongye Chen, Huishu Yuan & Ning Lang

  2. Department of Radiology, Peking University International Hospital, Beijing, People’s Republic of China

    Enlong Zhang

  3. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, People’s Republic of China

    Nan Li

Authors
  1. Jiahui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongye Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enlong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huishu Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ning Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ning Lang.

Ethics declarations

Conflict of interest

None of the authors has any potential conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PPTX 190 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Chen, Y., Zhang, E. et al. Use of monoexponential diffusion-weighted imaging and diffusion kurtosis imaging and dynamic contrast-enhanced-MRI for the differentiation of spinal tumors. Eur Spine J 29, 1112–1120 (2020). https://doi.org/10.1007/s00586-020-06330-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00586-020-06330-w

Keywords

Search

Navigation