Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT

  • Jiahui Zhang
  • Yongye Chen
  • Yanyan Zhang
  • Enlong Zhang
  • Hon J. Yu
  • Huishu Yuan
  • Yang Zhang
  • Min-Ying SuEmail author
  • Ning LangEmail author
Original Article



To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and 18F-FDG PET/CT in spinal tumors, and their role in differential diagnosis.


A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters Ktrans and kep. On PET, the maximum standardized uptake value SUVmax was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters.


The Ktrans, kep and SUVmax were not statistically different among malignant, benign and borderline groups (P = 0.95, 0.50, 0.11). There was no significant correlation between Ktrans and SUVmax (r = − 0.20, P = 0.18), or between kep and SUVmax (r = − 0.16, P = 0.28). The kep was significantly higher in plasmacytoma than in metastasis (0.78 ± 0.17 vs. 0.61 ± 0.18, P = 0.02); in contrast, the SUVmax was significantly lower in plasmacytoma than in metastasis (5.58 ± 2.16 vs. 9.37 ± 4.26, P = 0.03). In differential diagnosis, the AUC of kep and SUVmax was 0.79 and 0.78, respectively.


The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions.

Graphic abstract

These slides can be retrieved under Electronic Supplementary Material.


Magnetic resonance imaging Positron emission tomography/computed tomography Spinal neoplasms 



Area under the curve


Computed tomography


Dynamic-contrast-enhanced magnetic resonance imaging


Diffusion-weighted imaging




Positron emission tomography


Receiver operating characteristic


Region of interest


Signal enhancement


Standardized uptake value


Repetition time


Echo time



This study was supported by National Natural Science Foundation of China (81701648 and 81971578), NIH R01 CA127927 and Key Clinical Projects of the Peking University Third Hospital (BYSY2018007).

Compliance with ethical standards

Conflict of interest

None of the authors has any potential conflict of interest.

Supplementary material

586_2019_6213_MOESM1_ESM.pptx (9.7 mb)
Supplementary material 1 (PPTX 9897 kb)
586_2019_6213_MOESM2_ESM.docx (451 kb)
Supplementary material 2 (DOCX 450 kb)


  1. 1.
    Lang P, Honda G, Roberts T, Vahlensieck M, Johnston JO, Rosenau W et al (1995) Musculoskeletal neoplasm: perineoplastic edema versus tumor on dynamic postcontrast MR imaging with spatial mapping of instantaneous enhancement rates. Radiology 197:831–839CrossRefGoogle Scholar
  2. 2.
    Moulopoulos LA, Maris TG, Papanikolaou N, Panagi G, Vlahos L, Dimopoulos MA (2003) Detection of malignant bone marrow involvement with dynamic contrast-enhanced magnetic resonance imaging. Ann Oncol 14(1):152–158CrossRefGoogle Scholar
  3. 3.
    Lang N, Yuan H, Yu HJ, Su MY (2017) Diagnosis of spinal lesions using heuristic and pharmacokinetic parameters measured by dynamic contrast-enhanced MRI. Acad Radiol 24(7):867–875CrossRefGoogle Scholar
  4. 4.
    Lang N, Su MY, Xing X, Yu HJ, Yuan H (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):1068–1075CrossRefGoogle Scholar
  5. 5.
    Lang N, Su MY, Yu HJ, Yuan H (2015) Differentiation of tuberculosis and metastatic cancer in the spine using dynamic contrast-enhanced MRI. Eur Spine J 24:1729–1737CrossRefGoogle Scholar
  6. 6.
    Saha A, Peck KK, Lis E, Holodny AI, Yamada Y, Karimi S (2014) Magnetic resonance perfusion characteristics of hypervascular renal and hypovascular prostate spinal metastases: clinical utilities and implications. Spine 39:E1433–E1440CrossRefGoogle Scholar
  7. 7.
    Khadem NR, Karimi S, Peck KK, Yamada Y, Lis E, Lyo J 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 33:2178–2185CrossRefGoogle Scholar
  8. 8.
    Lang N, Su MY, Yu HJ, Lin M, Hamamura MJ, Yuan H (2013) Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI. Magn Reson Imaging 31:1285–1291CrossRefGoogle Scholar
  9. 9.
    Dammacco F, Rubini G, Ferrari C, Vacca A, Racanelli V (2015) 18F-FDG PET/CT: a review of diagnostic and prognostic features in multiple myeloma and related disorders. Clin Exp Med 15(1):1–18CrossRefGoogle Scholar
  10. 10.
    Al-Ibraheem A, Buck A, Krause BJ, Scheidhauer K, Schwaiger M (2009) Clinical applications of FDG PET and PET/CT in head and neck cancer. J Oncol 2009:208725CrossRefGoogle Scholar
  11. 11.
    Lee SH, Rimner A, Gelb E, Deasy JO, Hunt MA, Humm JL, Tyagi N (2018) Correlation between tumor metabolism and semiquantitative perfusion magnetic resonance imaging metrics in non-small cell lung cancer. Int J Radiat Oncol Biol Phys 102(4):718–726CrossRefGoogle Scholar
  12. 12.
    Surov A, Leifels L, Meyer HJ, Winter K, Sabri O, Purz S (2018) Associations between histogram analysis DCE MRI parameters and complex 18F-FDG-PET values in head and neck squamous cell carcinoma. Anticancer Res 38(3):1637–1642PubMedGoogle Scholar
  13. 13.
    Feng F, Qiang F, Shen A, Shi D, Fu A, Li H (2018) Dynamic contrast-enhanced MRI versus 18F-FDG PET/CT: which is better in differentiation between malignant and benign solitary pulmonary nodules? Chin J Cancer Res 30(1):21–30CrossRefGoogle Scholar
  14. 14.
    Leifels L, Purz S, Stumpp P, Schob S, Meyer HJ, Kahn T (2017) Associations between 18F-FDG-PET, DWI, and DCE parameters in patients with head and neck squamous cell carcinoma depend on tumor grading. Contrast Media Mol Imaging 2017:5369625CrossRefGoogle Scholar
  15. 15.
    Han M, Kim S, Lee S, Choi JW (2015) The correlations between MRI perfusion, diffusion parameters, and 18F-FDG PET metabolic parameters in primary head-and-neck cancer: a cross-sectional analysis in single institute. Medicine 94(47):e2141CrossRefGoogle Scholar
  16. 16.
    Calcagno C, Ramachandran S, Izquierdo-Garcia D et al (2013) The complementary roles of dynamic contrast-enhanced MRI and 18F-fluorodeoxyglucose PET/CT for imaging of carotid atherosclerosis. Eur J Nuclear Med Mol Imaging 40(12):1884–1893CrossRefGoogle Scholar
  17. 17.
    Tofts PS (1997) Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 7(1):91–101CrossRefGoogle Scholar
  18. 18.
    Wong KH, Panek R, Dunlop A, Mcquaid D, Riddell A, Welsh LC et al (2018) Changes in multimodality functional imaging parameters early during chemoradiation predict treatment response in patients with locally advanced head and neck cancer. Eur J Nuclear Med Mol Imaging 45(5):759–767CrossRefGoogle Scholar
  19. 19.
    An YY, Kim SH, Kang BJ, Lee AW (2015) Treatment response evaluation of breast cancer after neoadjuvant chemotherapy and usefulness of the imaging parameters of MRI and PET/CT. J Korean Med Sci 30(6):808–815CrossRefGoogle Scholar
  20. 20.
    Cho N, Im SA, Cheon GJ, Park IA, Lee KH, Kim TY et al (2018) Integrated 18F-FDG PET/MRI in breast cancer: early prediction of response to neoadjuvant chemotherapy. Eur J Nuclear Med Mol Imaging 45(3):328–339CrossRefGoogle Scholar
  21. 21.
    Sarabhai T, Tschischka A, Stebner V, Nensa F, Wetter A, Kimmig R et al (2018) Simultaneous multiparametric PET/MRI for the assessment of therapeutic response to chemotherapy or concurrent chemoradiotherapy of cervical cancer patients: preliminary results. Clin Imaging 49:163–168CrossRefGoogle Scholar
  22. 22.
    Bowen SR, Yuh WTC, Hippe DS, Wu W, Partridge SC, Elias S et al (2018) Tumor radiomic heterogeneity: multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy. J Magn Reson Imaging 47(5):1388–1396CrossRefGoogle Scholar
  23. 23.
    Chen BB, Tien YW, Chang MC, Cheng MF, Chang YT, Yang SH et al (2018) Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer. Eur J Nuclear Med Mol Imaging 45(7):1205–1217CrossRefGoogle Scholar
  24. 24.
    Ng SH, Liao CT, Lin CY, Chan SC, Lin YC, Yen TC et al (2016) Dynamic contrast-enhanced MRI, diffusion-weighted MRI and 18F-FDG PET/CT for the prediction of survival in oropharyngeal or hypopharyngeal squamous cell carcinoma treated with chemoradiation. Eur Radiol 26(11):4162–4172CrossRefGoogle Scholar
  25. 25.
    Chan SC, Cheng NM, Hsieh CH, Ng SH, Lin CY, Yen TC et al (2017) Multiparametric imaging using 18F-FDG PET/CT heterogeneity parameters and functional MRI techniques: prognostic significance in patients with primary advanced oropharyngeal or hypopharyngeal squamous cell carcinoma treated with chemoradiotherapy. Oncotarget 8(37):62606–62621CrossRefGoogle Scholar
  26. 26.
    Lim I, Noh WC, Park J, Park JA, Kim HA, Kim EK et al (2014) The combination of FDG PET and dynamic contrast-enhanced MRI improves the prediction of disease-free survival in patients with advanced breast cancer after the first cycle of neoadjuvant chemotherapy. Eur J Nuclear Med Mol Imaging 41(10):1852–1860CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of RadiologyPeking University Third HospitalBeijingPeople’s Republic of China
  2. 2.Department of Nuclear MedicinePeking University Third HospitalBeijingPeople’s Republic of China
  3. 3.Department of Radiological Sciences, Center for Functional Onco-ImagingUniversity of CaliforniaIrvineUSA

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