, Volume 61, Issue 8, pp 861–867 | Cite as

Correlation of post-contrast T1-weighted MRI surface regularity, tumor bulk, and necrotic volume with Ki67 and p53 in glioblastomas

  • Adam Hasse
  • Mark Dapash
  • Yong Jeong
  • Sameer A. Ansari
  • Timothy J. Carroll
  • Maciej Lesniak
  • Daniel Thomas GinatEmail author
Diagnostic Neuroradiology



p53 and Ki67 status can be relevant to the management of glioblastoma. The goal of this study is to determine whether tumor morphology and bulk depicted on MRI correlate with p53 and Ki67 in glioblastoma.


A retrospective review of 223 patients with glioblastoma and corresponding p53 or Ki67 status, along with T1-weighted post-contrast MR images was performed. Enhancing tumors were outlined for determining surface regularity, tumor bulk, and necrotic volume. The median value of 0.1 was chosen for p53 and 0.2 for Ki67 to separate each data set into two classes. T tests and receiver operating characteristic analysis were performed to determine the separation of the classes and the predicting power of each feature.


There were significant differences between tumor surface regularity (p = 0.01) and necrotic volume (p = 0.0429) according to Ki67 levels, although neither had statistically significant predictive power (AUC = 0.697, p = 0.0506 and AUC = 0.577, p = 0.164, respectively). There were also significant differences between tumor bulk (p = 0.0239) and necrotic volume (p = 0.0200) according to p53 levels, but again no significant predictive power was found using ROC analysis (AUC = 0.5882, p = 0.0894 and AUC = 0.567, p = 0.155, respectively).


Quantitative morphological tumor characteristics on post-contrast T1-weighted MRI can to a certain degree provide insights regarding Ki67 and p53 status in patients with glioblastoma.


Glioblastoma Ki67 p53 Surface regularity Tumor bulk Necrotic volume 


Compliance with ethical standards


This work is supported, in part, by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Grant no. T32 EB002103.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study formal consent is not required.

Informed consent

For this type of study formal consent is not required.


  1. 1.
    Olar A, Aldape KD (2014) Using the molecular classification of glioblastoma to inform personalized treatment. J Pathol 232:165–177CrossRefGoogle Scholar
  2. 2.
    Touat M, Idbaih A, Sanson M, Ligon KL (2017) Glioblastoma targeted therapy: updated approaches from recent biological insights. Ann Oncol 28:1457–1472CrossRefGoogle Scholar
  3. 3.
    Bosnyák E, Michelhaugh SK, Klinger NV, Kamson DO, Barger GR, Mittal S, Juhász C (2017) Prognostic molecular and imaging biomarkers in primary glioblastoma. Clin Nucl Med 42:341–347CrossRefGoogle Scholar
  4. 4.
    Zhang Y, Dube C, Gibert M Jr (2018) The p53 pathway in glioblastoma. Cancers (Basel)10 pii: E297Google Scholar
  5. 5.
    England B, Huang T, Karsy M (2013) Current understanding of the role and targeting of tumor suppressor p53 in glioblastoma multiforme. Tumour Biol 34:2063–2074CrossRefGoogle Scholar
  6. 6.
    Schröder R, Feisel KD, Ernestus RI (2002) Ki-67 labeling is correlated with the time to recurrence in primary glioblastomas. J Neuro-Oncol 56:127–132CrossRefGoogle Scholar
  7. 7.
    de Azambuja E, Cardoso F, de Castro G, Colozza M, Mano MS, Durbecq V, Sotiriou C, Larsimont D, Piccart-Gebhart MJ, Paesmans M (2007) Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients. Br J Cancer 96:1504–1513CrossRefGoogle Scholar
  8. 8.
    Pérez-Beteta J, Molina-García D, Ortiz-Alhambra JA, Fernández-Romero A, Luque B, Arregui E, Calvo M, Borrás JM, Meléndez B, Rodríguez de Lope Á, Moreno de la Presa R, Iglesias Bayo L, Barcia JA, Martino J, Velásquez C, Asenjo B, Benavides M, Herruzo I, Revert A, Arana E, Pérez-García VM (2018) Tumor surface regularity at MR imaging predicts survival and response to surgery in patients with glioblastoma. Radiology 288:218–225CrossRefGoogle Scholar
  9. 9.
    Grossmann P, Gutman DA, Dunn WD Jr, Holder CA, Aerts HJ (2016) Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in glioblastoma. cBMC Cancer 16:611CrossRefGoogle Scholar
  10. 10.
    Dechristé G, Fehrenbach J, Griseti E, Lobjois V, Poignard C (2018) Viscoelastic modeling of the fusion of multicellular tumor spheroids in growth phase. J Theor Biol 454:102–109CrossRefGoogle Scholar
  11. 11.
    Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, Miller CR, Ding L, Golub T, Mesirov JP, Alexe G, Lawrence M, O'Kelly M, Tamayo P, Weir BA, Gabriel S, Winckler W, Gupta S, Jakkula L, Feiler HS, Hodgson JG, James CD, Sarkaria JN, Brennan C, Kahn A, Spellman PT, Wilson RK, Speed TP, Gray JW, Meyerson M, Getz G, Perou CM, Hayes DN, Cancer Genome Atlas Research Network (2010) An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1. Cancer Cell 17:98–110CrossRefGoogle Scholar
  12. 12.
    Balogh GA, Mailo D, Nardi H et al (2010) Serological levels of mutated p53 protein are highly detected at early stages in breast cancer patients. Exp Ther Med 1:357–361CrossRefGoogle Scholar
  13. 13.
    Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE (1996) Prognostic significance of preoperative MRI scans in glioblastoma multiforme. J Neuro-Oncol 27:65–73CrossRefGoogle Scholar
  14. 14.
    Rios Velazquez E, Meier R, Dunn WD Jr et al (2015) Fully automatic GBM segmentation in the TCGA-GBM dataset: prognosis and correlation with VASARI features. Sci Rep 5:16822CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Graduate Program in Medical PhysicsUniversity of ChicagoChicagoUSA
  2. 2.Pritzker School of MedicineUniversity of ChicagoChicagoUSA
  3. 3.Department of Biomedical EngineeringNorthwestern UniversityChicagoUSA
  4. 4.Department of RadiologyNorthwestern UniversityChicagoUSA
  5. 5.Department of RadiologyUniversity of ChicagoChicagoUSA
  6. 6.Department of NeurosurgeryNorthwestern UniversityChicagoUSA

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