Skip to main content

Advertisement

SpringerLink
Log in

Volumetric assessment of glioblastoma and its predictive value for survival

  • Original Article - Brain Tumors
  • Published:
Acta Neurochirurgica Aims and scope Submit manuscript

Abstract

Background

The objective of this study was to evaluate the morphology of glioblastoma on structural pretreatment magnetic resonance imaging (MRI), defining imaging prognostic factors.

Method

We conducted a retrospective analysis of MR images from 114 patients harboring a primary glioblastoma, derived from two neurosurgical departments. Tumor segmentation was carried out in a semi-automated fashion. Tumor compartments comprised contrast-enhancing volume (CEV+), perifocal hyperintensity on fluid-attenuated inversion recovery (FLAIR) images (FLAIR+) excluding CEV+, and a non-enhancing area within the CEV+ lesion (CEV−). Additionally, two ratios were calculated from these volumes, the edema-tumor ratio (ETR) and necrosis-tumor ratio (NTR). All patients received surgical resection, followed by concomitant radiation and chemotherapy.

Results

Tumor segmentation revealed the strongest correlation between the CEV+ volume and the CEV−, presenting intratumoral necrosis (p < 0.001). The relation between the tumor surrounding the FLAIR+ area and the CEV+ volume and the ETR is inversely correlated (p = 0.001). The most important prognostic factor in multivariable analysis was NTR (HR 2.63, p = 0.016). The cut-off value in our cohort for NTR was 0.33, equivalent to a decrease in survival if the necrotic core of the tumor (CEV−) accounts for more than 33% of the tumor mass itself (CEV+).

Conclusions

Our data emphasizes the importance of the necrosis-tumor ratio as a biomarker in glioblastoma imaging, rather than single tumor compartment volumes. NTR can help to identify a subset of tumors with a higher resistance to therapy and a dismal prognosis.

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

References

  1. Bauchet L, Mathieu-Daudé H, Fabbro-Peray P, Rigau V, Fabbro M, Chinot O, Pallusseau L, Carnin C, Lainé K, Schlama A, Thiebaut A, Patru MC, Bauchet F, Lionnet M, Wager M, Faillot T, Taillandier L, Figarella-Branger D, Capelle L, Loiseau H, Frappaz D, Campello C, Kerr C, Duffau H, Reme-Saumon M, Trétarre B, Daures J-P, Henin D, Labrousse F, Menei P, Honnorat J (2010) Oncological patterns of care and outcome for 952 patients with newly diagnosed glioblastoma in 2004. Neuro-Oncology 12(7):725–735

    Article  PubMed  PubMed Central  Google Scholar 

  2. Belden CJ, Valdes PA, Ran C, Pastel DA, Harris BT, Fadul CE, Israel MA, Paulsen K, Roberts DW (2011) Genetics of glioblastoma: a window into its imaging and histopathologic variability. Radiographics 31(6):1717–1740

    Article  PubMed  Google Scholar 

  3. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383

    Article  CAS  PubMed  Google Scholar 

  4. Chen L, Voronovich Z, Clark K, Hands I, Mannas J, Walsh M, Nikiforova MN, Durbin EB, Weiss H, Horbinski C (2014) Predicting the likelihood of an isocitrate dehydrogenase 1 or 2 mutation in diagnoses of infiltrative glioma. Neuro-Oncology 16(11):1478–1483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Davies DC (2002) Blood-brain barrier breakdown in septic encephalopathy and brain tumours. J Anat 200(6):639–646

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Eads CA, Danenberg KD, Kawakami K, Saltz LB, Blake C, Shibata D, Danenberg PV, Laird PW (2000) MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 28(8):E32

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Eidel O, Burth S, Neumann J-O, Kieslich PJ, Sahm F, Jungk C, Kickingereder P, Bickelhaupt S, Mundiyanapurath S, Baumer P, Wick W, Schlemmer H-P, Kiening K, Unterberg A, Bendszus M, Radbruch A (2017) Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: a correlation with histopathology. PLoS One 12(1):e0169292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ellingson BM, Bendszus M, Boxerman J, Barboriak D, Erickson BJ, Smits M, Nelson SJ, Gerstner E, Alexander B, Goldmacher G, Wick W, Vogelbaum M, Weller M, Galanis E, Kalpathy-Cramer J, Shankar L, Jacobs P, Pope WB, Yang D, Chung C, Knopp MV, Cha S, van den Bent MJ, Chang S, Yung WKA, Cloughesy TF, Wen PY, Gilbert MR (2015) Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro-Oncology 17(9):1188–1198

    PubMed  PubMed Central  Google Scholar 

  9. Fyllingen EH, Stensjøen AL, Berntsen EM, Solheim O, Reinertsen I (2016) Glioblastoma segmentation: comparison of three different software packages. PLoS One 11(10):e0164891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE (1996) Prognostic significance of preoperative MRI scans in glioblastoma multiforme. J Neuro-Oncol 27(1):65–73

    Article  CAS  Google Scholar 

  11. Han Y, Yan L-F, Wang X-B, Sun Y-Z, Zhang X, Liu Z-C, Nan H-Y, Hu Y-C, Yang Y, Zhang J, Yu Y, Sun Q, Tian Q, Hu B, Xiao G, Wang W, Cui G-B (2018) Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis. BMC Cancer 18(1):215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hegi ME, Diserens A-C, Gorlia T, Hamou M-F, de Tribolet N, Weller M, Kros JM, Hainfellner JA, Mason W, Mariani L, Bromberg JEC, Hau P, Mirimanoff RO, Cairncross JG, Janzer RC, Stupp R (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352(10):997–1003

    Article  CAS  PubMed  Google Scholar 

  13. Henker C, Kriesen T, Glass Ä, Schneider B, Piek J (2017) Volumetric quantification of glioblastoma. Experiences with different measurement techniques and impact on survival. J Neuro-Oncol 135(2):391–402

    Article  CAS  Google Scholar 

  14. Kanas VG, Zacharaki EI, Thomas GA, Zinn PO, Megalooikonomou V, Colen RR (2017) Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput Methods Prog Biomed 140:249–257

    Article  Google Scholar 

  15. Karnofsky DA, Abelmann WH, Craver LF, Burchenal JH (1948) The use of the nitrogen mustards in the palliative treatment of carcinoma. With particular reference to bronchogenic carcinoma. Cancer 1(4):634–656

    Article  Google Scholar 

  16. Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ (1987) Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg 66(6):865–874

    Article  CAS  PubMed  Google Scholar 

  17. Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95(2):190–198

    Article  CAS  PubMed  Google Scholar 

  18. Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH (2017) Quality of radiomic features in glioblastoma multiforme: impact of semi-automated tumor segmentation software. Korean J Radiol 18(3):498–509

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lemée J-M, Clavreul A, Menei P (2015) Intratumoral heterogeneity in glioblastoma: don’t forget the peritumoral brain zone. Neuro-Oncology 17(10):1322–1332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Li YM, Suki D, Hess K, Sawaya R (2016) The influence of maximum safe resection of glioblastoma on survival in 1229 patients: can we do better than gross-total resection? J Neurosurg 124(4):977–988

    Article  PubMed  Google Scholar 

  21. Mabray MC, Barajas RF, Cha S (2015) Modern brain tumor imaging. Brain Tumor Res Treat 3(1): 8–23

  22. Norden AD, Young GS, Setayesh K, Muzikansky A, Klufas R, Ross GL, Ciampa AS, Ebbeling LG, Levy B, Drappatz J, Kesari S, Wen PY (2008) Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology 70(10):779–787

    Article  CAS  PubMed  Google Scholar 

  23. Osswald M, Jung E, Sahm F, Solecki G, Venkataramani V, Blaes J, Weil S, Horstmann H, Wiestler B, Syed M, Huang L, Ratliff M, Karimian Jazi K, Kurz FT, Schmenger T, Lemke D, Gömmel M, Pauli M, Liao Y, Häring P, Pusch S, Herl V, Steinhäuser C, Krunic D, Jarahian M, Miletic H, Berghoff AS, Griesbeck O, Kalamakis G, Garaschuk O, Preusser M, Weiss S, Liu H, Heiland S, Platten M, Huber PE, Kuner T, von Deimling A, Wick W, Winkler F (2015) Brain tumour cells interconnect to a functional and resistant network. Nature 528(7580):93–98

    Article  CAS  PubMed  Google Scholar 

  24. Ostrom QT, Gittleman H, Stetson L, Virk S, Barnholtz-Sloan JS (2018) Epidemiology of intracranial gliomas. Prog Neurol Surg 30:1–11

    Article  PubMed  Google Scholar 

  25. Petrecca K, Guiot M-C, Panet-Raymond V, Souhami L (2013) Failure pattern following complete resection plus radiotherapy and temozolomide is at the resection margin in patients with glioblastoma. J Neuro-Oncol 111(1): 19–23

  26. Pope WB, Sayre J, Perlina A, Villablanca JP, Mischel PS, Cloughesy TF (2005) MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol 26(10):2466–2474

    PubMed  PubMed Central  Google Scholar 

  27. Price SJ, Jena R, Burnet NG, Hutchinson PJ, Dean AF, Peña A, Pickard JD, Carpenter TA, Gillard JH (2006) Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 27(9):1969–1974

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Ratel D, van der Sanden B, Wion D (2016) Glioma resection and tumor recurrence: back to Semmelweis. Neuro-Oncology 18(12):1688–1689

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sales AHA, Bette S, Barz M, Huber T, Wiestler B, Ryang Y-M, Schmidt-Graf F, Liesche F, Combs SE, Meyer B, Gempt J (2019) Role of postoperative tumor volume in patients with MGMT-unmethylated glioblastoma. J Neuro-Oncol

  30. Stupp R, Hegi ME, Mason WP, van den Bent, Martin J, Taphoorn MJB, Janzer RC, Ludwin SK, Allgeier A, Fisher B, Belanger K, Hau P, Brandes AA, Gijtenbeek J, Marosi C, Vecht CJ, Mokhtari K, Wesseling P, Villa S, Eisenhauer E, Gorlia T, Weller M, Lacombe D, Cairncross JG, Mirimanoff R-O (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10(5):459–466

    Article  CAS  PubMed  Google Scholar 

  31. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJB, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996

    Article  CAS  PubMed  Google Scholar 

  32. Tabatabai G, Hegi M, Stupp R, Weller M (2012) Clinical implications of molecular neuropathology and biomarkers for malignant glioma. Curr Neurol Neurosci Rep 12(3):302–307

    Article  CAS  PubMed  Google Scholar 

  33. Tamura R, Ohara K, Sasaki H, Morimoto Y, Yoshida K, Toda M (2018) Histopathological vascular investigation of the peritumoral brain zone of glioblastomas. J Neuro-Oncol 136(2):233–241

    Article  CAS  Google Scholar 

  34. Taylor EN, Ding Y, Zhu S, Cheah E, Alexander P, Lin L, Aninwene GE, Hoffman MP, Mahajan A, Mohamed ASR, McDannold N, Fuller CD, Chen CC, Gilbert RJ (2017) Association between tumor architecture derived from generalized Q-space MRI and survival in glioblastoma. Oncotarget 8(26):41815–41826

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wang Q, Hu B, Hu X, Kim H, Squatrito M, Scarpace L, deCarvalho AC, Lyu S, Li P, Li Y, Barthel F, Cho HJ, Lin Y-H, Satani N, Martinez-Ledesma E, Zheng S, Chang E, Sauvé C-EG, Olar A, Lan ZD, Finocchiaro G, Phillips JJ, Berger MS, Gabrusiewicz KR, Wang G, Eskilsson E, Hu J, Mikkelsen T, DePinho RA, Muller F, Heimberger AB, Sulman EP, Nam D-H, Verhaak RGW (2017) Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32(1):42–56 e6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R (2018) Radiomics in brain tumor. Image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 39(2):208–216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Prof. Günther Kundt for his valuable statistical suggestions and the patients who participated in this study.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, University Medicine of Rostock, Schillingallee 35, 18055, Rostock, Germany

    Christian Henker, Marie Cristin Hiepel, Thomas Kriesen & Jürgen Piek

  2. Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany

    Moritz Scherer, Christel Herold-Mende & Andreas Unterberg

  3. Institute for Biostatistics and Informatics in Medicine, University Medicine of Rostock, Rostock, Germany

    Änne Glass

  4. Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany

    Martin Bendszus

  5. Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medicine of Rostock, Rostock, Germany

    Sönke Langner & Marc-André Weber

  6. Institute for Pathology, University Medicine of Rostock, Rostock, Germany

    Björn Schneider

Authors
  1. Christian Henker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marie Cristin Hiepel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Kriesen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Moritz Scherer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Änne Glass
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christel Herold-Mende
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Martin Bendszus
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sönke Langner
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marc-André Weber
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Björn Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Andreas Unterberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jürgen Piek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Henker.

Ethics declarations

Informed consent was obtained from all individual participants included in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of both institutional research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards (registration number 005/2003, respectively).

Additional information

Publisher’s note

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

This article is part of the Topical Collection on Brain Tumors

Electronic supplementary material

ESM 1

(DOCX 19 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Henker, C., Hiepel, M.C., Kriesen, T. et al. Volumetric assessment of glioblastoma and its predictive value for survival. Acta Neurochir 161, 1723–1732 (2019). https://doi.org/10.1007/s00701-019-03966-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00701-019-03966-6

Keywords

Search

Navigation