Journal of Neuro-Oncology

, Volume 139, Issue 3, pp 731–738 | Cite as

Post-gadolinium 3-dimensional spatial, surface, and structural characteristics of glioblastomas differentiate pseudoprogression from true tumor progression

  • Madison R. Hansen
  • Edward Pan
  • Andrew Wilson
  • Morgan McCreary
  • Yeqi Wang
  • Thomas Stanley
  • Marco C. Pinho
  • Xiaohu Guo
  • Darin T. OkudaEmail author
Clinical Study



Pseudoprogression is often indistinguishable from true tumor progression on conventional 2-dimensional (2D) MRI in glioblastoma multiforme (GBM) patients. The aim of this study was to determine the association between post-gadolinium 3-dimensional (3D) characteristics and clinical state in GBM patients.


Standardized 3D brain MRI studies were performed, and contrast enhancing portions of each tumor were segmented and analyzed, blinded to clinical state, using principal component analysis (PCA), medial axis transformation (MAT), and coverage analysis. Associations between the 3D characteristics of the post-gadolinium enhanced regions and the clinical status of patients were performed.


A total of 15 GBM patients [male: 11 (73%); median age (range): 62 years (36–72)] with a median disease duration of 6 months (range 2–24 months) were studied cross-sectionally with 6 (40%) patients identified with tumor progression. Post-gadolinium features corresponding to the group with progressive disease exhibited a more spherical and symmetric shape relative to their stable counterparts (p = 0.005). The predictive value of a more uniformly full post-gadolinium enhanced shell to clinical progression was determined with a sensitivity of 66.7% (95% CI 29.9–92.5), specificity of 100% (54.1–100), and PPV of 100% (p = 0.028, 2-tailed Fisher’s exact test). There did not appear to be an association between the thickness of the contrast enhanced shell to clinical state.


The application of 3D technology with post-gadolinium imaging data may inform healthcare providers with new insights into disease states based on spatial, surface, and structural patterns.


Glioblastoma multiforme 3-Dimensional MRI Contrast enhancement Pseudoprogression 



We thank all patients who participated in this research effort.

Data availabilty

The datasets generated and analysed during this study are available from the corresponding author on reasonable request.

Compliance with ethical standards

Conflict of interest

D.O. received lecture fees from Acorda Therapeutics, Genentech, Genzyme, and Teva, advisory and consulting fees from Celgene, EMD Serono, Genentech, Genzyme, and Novartis and research support from Biogen. M.H., E.P., A.W., M.M., Y.W., T.S., M.P, and X.G. report no disclosures.

Informed consent

Informed consent was obtained from all study participants.


  1. 1.
    Ostrom QT et al (2015) Epidemiology of gliomas. Cancer Treat Res 163:1–14CrossRefPubMedGoogle Scholar
  2. 2.
    Ostrom QT et al (2015) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro Oncol 17(Suppl 4):iv1–iv62CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wen PY et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28(11):1963–1972CrossRefPubMedGoogle Scholar
  4. 4.
    Barajas RF Jr et al (2010) Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology 254(2):564–576CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Zaki HS et al (2004) Vanishing contrast enhancement in malignant glioma after corticosteroid treatment. Acta Neurochir 146(8):841–845CrossRefPubMedGoogle Scholar
  6. 6.
    de Groot JF et al (2010) Tumor invasion after treatment of glioblastoma with bevacizumab: radiographic and pathologic correlation in humans and mice. Neuro Oncol 12(3):233–242CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Brandes AA et al (2008) MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. J Clin Oncol 26(13):2192–2197CrossRefPubMedGoogle Scholar
  8. 8.
    Brandsma D et al (2008) Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 9(5):453–461CrossRefPubMedGoogle Scholar
  9. 9.
    Aquino D et al (2017) MRI in glioma immunotherapy: evidence, pitfalls, and perspectives. J Immunol Res 2017:5813951CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Okada H et al (2015) Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol 16(15):e534–e542CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Newton BD et al (2017) Three-dimensional shape and surface features distinguish multiple sclerosis lesions from nonspecific white matter disease. J Neuroimaging 27(6):613–619CrossRefPubMedGoogle Scholar
  12. 12.
    Louis DN et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820CrossRefPubMedGoogle Scholar
  13. 13.
    Pan Li BW, Feng Sun X, Guo C, Zhang, Wenping, Wang (2015) Q-mat: Computing medial axis transform by quadratic error minimization. ACM Trans Graphics (TOG) 35(1):8Google Scholar
  14. 14.
    Youland RS et al (2018) Prospective trial evaluating the sensitivity and specificity of 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-DOPA) PET and MRI in patients with recurrent gliomas. J Neuro-oncol, 137(3):583–591CrossRefGoogle Scholar
  15. 15.
    Jena A et al (2016) Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-fluoroethyl)-L-tyrosine PET/MRI. Clin Nucl Med 41(5):e228–e236CrossRefPubMedGoogle Scholar
  16. 16.
    Reimer C et al (2017) Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps. PLoS ONE 12(4):e0174620CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Kim TH et al (2017) Combined use of susceptibility weighted magnetic resonance imaging sequences and dynamic susceptibility contrast perfusion weighted imaging to improve the accuracy of the differential diagnosis of recurrence and radionecrosis in high-grade glioma patients. Oncotarget 8(12):20340–20353CrossRefPubMedGoogle Scholar
  18. 18.
    Suh CH et al (2013) Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging. AJNR Am J Neuroradiol 34(12):2278–2286CrossRefPubMedGoogle Scholar
  19. 19.
    Kinoshita M et al (2016) Introduction of high throughput magnetic resonance T2-weighted image texture analysis for WHO grade 2 and 3 gliomas. PLoS ONE 11(10):e0164268CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Skogen K et al (2016) Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol 85(4):824–829CrossRefPubMedGoogle Scholar
  21. 21.
    Zacharaki EI et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62(6):1609–1618CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Pallud J et al (2009) Prognostic significance of imaging contrast enhancement for WHO grade II gliomas. Neuro-Oncology 11(2):176–182CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Reddy K, Westerly D, Chen C (2013) MRI patterns of T1 enhancing radiation necrosis versus tumour recurrence in high-grade gliomas. J Med Imaging Radiat Oncol 57(3):349–355CrossRefPubMedGoogle Scholar
  24. 24.
    Kumar AJ et al (2000) Malignant gliomas: MR imaging spectrum of radiation therapy- and chemotherapy-induced necrosis of the brain after treatment. Radiology 217(2):377–384CrossRefPubMedGoogle Scholar
  25. 25.
    Aiken AH et al (2008) Longitudinal magnetic resonance imaging features of glioblastoma multiforme treated with radiotherapy with or without brachytherapy. Int J Radiat Oncol Biol Phys 72(5):1340–1346CrossRefPubMedGoogle Scholar
  26. 26.
    Wan Y et al (2013) Proliferation and migration of tumor cells in tapered channels. Biomed Microdevices 15(4):635–643CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Stensjoen AL et al (2015) Growth dynamics of untreated glioblastomas in vivo. Neuro-Oncology 17(10):1402–1411CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Madison R. Hansen
    • 1
  • Edward Pan
    • 2
  • Andrew Wilson
    • 3
  • Morgan McCreary
    • 4
  • Yeqi Wang
    • 3
  • Thomas Stanley
    • 3
  • Marco C. Pinho
    • 5
  • Xiaohu Guo
    • 3
  • Darin T. Okuda
    • 1
    Email author
  1. 1.Neuroinnovation Program, Multiple Sclerosis & Neuroimmunology Imaging Program, Department of Neurology & Neurotherapeutics, UT Southwestern Medical CenterClinical Center for Multiple SclerosisDallasUSA
  2. 2.Department of Neurology and Neurotherapeutics, UT Southwestern Medical CenterSimmons Comprehensive Cancer CenterDallasUSA
  3. 3.Department of Computer ScienceUniversity of Texas at DallasDallasUSA
  4. 4.Department of Statistical ScienceBaylor UniversityWacoUSA
  5. 5.Department of RadiologySouthwestern Medical CenterDallasUSA

Personalised recommendations