Skip to main content

Advertisement

SpringerLink
Log in

Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

This study was to project a powerful volumetric-related parameter on magnetic resonance imaging (MRI) for classifying patients with glioblastoma multiforme (GBM) into distinct subgroups objectively. The preoperative MRIs of 147 patients with primary GBM were analyzed. Volumetric-related parameters, including V1 (tumor volume), V2 (peritumoral T2/FLAIR hyperintense volume) and V2/V1 (the volume ratio), were estimated by an ellipsoid model. Log-rank analysis and Cox regression methods were used to compare Kaplan–Meier plots and identified prognostic parameters. Log-rank analysis revealed that V1 and V2 were correlated with survival, but the P value was marginally significant (P = 0.082, P = 0.091, for progression-free survival [PFS]; P = 0.120, P = 0.073, for overall survival [OS], respectively). V2/V1 was a potential prognostic factor for both PFS and OS (P < 0.001 and P < 0.001, respectively). Cox regression analysis documented that higher V2/V1 (ratio ≥ 7.0) was independent unfavorable prognostic factor. The odd ratio (OR) of higher V2/V1 was 2.662 (95 % confidence interval [CI], 1.782–3.975; P < 0.001) for PFS and 3.450 (95 % CI, 2.079–5.725; P < 0.001) for OS, respectively. The volumetric-related parameters of V1, V2 and V2/V1 were helpful for predicting the prognosis of patients with GBM. V2/V1 was a more comprehensive and systematic prognostic factor in GBM patient, especially for those with small tumor but large peritumoral T2 hyperintense or large tumor but small peritumoral T2 hyperintense.

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

Similar content being viewed by others

References

  1. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, 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 

  2. Laws ER, Parney IF, Huang W, Anderson F, Morris AM, Asher A, Lillehei KO, Bernstein M, Brem H, Sloan A, Berger MS, Chang S (2003) Survival following surgery and prognostic factors for recently diagnosed malignant glioma: data from the Glioma Outcomes Project. J Neurosurg 99(3):467–473

    Article  PubMed  Google Scholar 

  3. Surawicz TS, Davis F, Freels S, Laws ER Jr, Menck HR (1998) Brain tumor survival: results from the National Cancer Data Base. J Neurooncol 40(2):151–160

    Article  CAS  PubMed  Google Scholar 

  4. Kaur G, Bloch O, Jian BJ, Kaur R, Sughrue ME, Aghi MK, McDermott MW, Berger MS, Chang SM, Parsa AT (2011) A critical evaluation of cystic features in primary glioblastoma as a prognostic factor for survival. J Neurosurg 115(4):754–759

    Article  PubMed  Google Scholar 

  5. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, 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 RO (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 

  6. Boots-Sprenger SH, Sijben A, Rijntjes J, Tops BB, Idema AJ, Rivera AL, Bleeker FE, Gijtenbeek AM, Diefes K, Heathcock L, Aldape KD, Jeuken JW, Wesseling P (2013) Significance of complete 1p/19q co-deletion, IDH1 mutation and MGMT promoter methylation in gliomas: use with caution. Mod Pathol 26(7):922–929

    Article  CAS  PubMed  Google Scholar 

  7. Chaichana K, Parker S, Olivi A, Quinones-Hinojosa A (2010) A proposed classification system that projects outcomes based on preoperative variables for adult patients with glioblastoma multiforme. J Neurosurg 112(5):997–1004

    Article  PubMed  Google Scholar 

  8. 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  Google Scholar 

  9. Park JK, Hodges T, Arko L, Shen M, Dello Iacono D, McNabb A, Olsen Bailey N, Kreisl TN, Iwamoto FM, Sul J, Auh S, Park GE, Fine HA, Black PM (2010) Scale to predict survival after surgery for recurrent glioblastoma multiforme. J Clin Oncol 28(24):3838–3843

    Article  PubMed Central  PubMed  Google Scholar 

  10. Iliadis G, Kotoula V, Chatzisotiriou A, Televantou D, Eleftheraki AG, Lambaki S, Misailidou D, Selviaridis P, Fountzilas G (2012) Volumetric and MGMT parameters in glioblastoma patients: survival analysis. BMC Cancer 12:3

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109

    Article  PubMed Central  PubMed  Google Scholar 

  12. Lotsch D, Ghanim B, Laaber M, Wurm G, Weis S, Lenz S, Webersinke G, Pichler J, Berger W, Spiegl-Kreinecker S (2013) Prognostic significance of telomerase-associated parameters in glioblastoma: effect of patient age. Neuro Oncol 15(4):423–432

    Article  PubMed Central  PubMed  Google Scholar 

  13. Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Mischel PS, Pope WB (2011) Quantitative volumetric analysis of conventional MRI response in recurrent glioblastoma treated with bevacizumab. Neuro Oncol 13(4):401–409

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. 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 

  16. Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, Majumder S, Colen RR (2012) A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature. PLoS One 7(8):e41522

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Iliadis G, Selviaridis P, Kalogera-Fountzila A, Fragkoulidi A, Baltas D, Tselis N, Chatzisotiriou A, Misailidou D, Zamboglou N, Fountzilas G (2009) The importance of tumor volume in the prognosis of patients with glioblastoma: comparison of computerized volumetry and geometric models. Strahlenther Onkol 185(11):743–750

    Article  PubMed  Google Scholar 

  18. Watanabe M, Tanaka R, Takeda N (1992) Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 34(6):463–469

    Article  CAS  PubMed  Google Scholar 

  19. Kelly PJ, Daumas-Duport C, Scheithauer BW, Kall BA, Kispert DB (1987) Stereotactic histologic correlations of computed tomography- and magnetic resonance imaging-defined abnormalities in patients with glial neoplasms. Mayo Clin Proc 62(6):450–459

    Article  CAS  PubMed  Google Scholar 

  20. Ft Earnest, Kelly PJ, Scheithauer BW, Kall BA, Cascino TL, Ehman RL, Forbes GS, Axley PL (1988) Cerebral astrocytomas: histopathologic correlation of MR and CT contrast enhancement with stereotactic biopsy. Radiology 166(3):823–827

    Article  Google Scholar 

  21. Hakyemez B, Erdogan C, Gokalp G, Dusak A, Parlak M (2010) Solitary metastases and high-grade gliomas: radiological differentiation by morphometric analysis and perfusion-weighted MRI. Clin Radiol 65(1):15–20

    Article  CAS  PubMed  Google Scholar 

  22. 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 

  23. Carlson MR, Pope WB, Horvath S, Braunstein JG, Nghiemphu P, Tso CL, Mellinghoff I, Lai A, Liau LM, Mischel PS, Dong J, Nelson SF, Cloughesy TF (2007) Relationship between survival and edema in malignant gliomas: role of vascular endothelial growth factor and neuronal pentraxin 2. Clin Cancer Res 13(9):2592–2598

    Article  CAS  PubMed  Google Scholar 

  24. Naeini KM, Pope WB, Cloughesy TF, Harris RJ, Lai A, Eskin A, Chowdhury R, Phillips HS, Nghiemphu PL, Behbahanian Y, Ellingson BM (2013) Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images. Neuro Oncol 15(5):626–634

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Simon R (2010) Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per Med 7(1):33–47

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by Beijing Natural Science Foundation (7122061).

Conflict of interest

No potential conflicts of interest were disclosed.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China

    Zhe Zhang, Haihui Jiang, Jiwei Bai, Yong Cui, Xiaohui Ren, Xiaolin Chen, Wei Zeng & Song Lin

  2. Department of Neuroimage, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China

    Xuzhu Chen

  3. Department of Pathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100050, China

    Junmei Wang

Authors
  1. Zhe Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haihui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuzhu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiwei Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaohui Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaolin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Junmei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wei Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Song Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Song Lin.

Additional information

Zhe Zhang and Haihui Jiang contributed equally to this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Jiang, H., Chen, X. et al. Identifying the survival subtypes of glioblastoma by quantitative volumetric analysis of MRI. J Neurooncol 119, 207–214 (2014). https://doi.org/10.1007/s11060-014-1478-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-014-1478-2

Keywords

Search

Navigation