Prognostic factors in patients with glioblastoma multiforme: focus on the pathologic variants

  • Ehsan AlimohammadiEmail author
  • Seyed Reza Bagheri
  • Alireza Sadeghsalehi
  • Parisa Rizevandi
  • Zahra Rezaie
  • Alireza Abdi
Original article


The aim of this study was to offer predicting factors for survival in adult patients with glioblastoma multiforme. 153 consecutive patients with high-grade glioma (WHO grade IV) were studied in Imam Reza hospital, Kermanshah University of Medical Science, Kermanshah, Iran, between April 2003 and April 2017. All patients treated with surgical resection and standard postoperative radiotherapy (54 Gy). Using the patients’ charts and electronic medical records system, the following data were obtained: gender, age, Karnofsky performance status (KPS) score on admission, primary vs. secondary type, extent of surgery, tumor location, tumor size, necrosis size, use of Temozolomide (TMZ), pathology subtype, and immunohistochemistry results. Patients were followed from the time of the surgery until the death occurred. Overall survival (OS) and progression-free survival (PFS) were calculated by the Kaplan–Meier method. Survival time curves for various subgroups were compared by the log-rank test. The impact of the suggested prognostic factors on survival was evaluated by univariate and multivariate analyses. Age, gender, KPS, extent of surgery, tumor location, necrosis size, and reoperation in recurrence had not any statistically significant effect on survival. Univariate analysis revealed a significant impact on outcome for pathology subtype (PFS: P < 0.001, OS: P < 0.001), tumor type (primary vs. secondary) (PFS: P = P < 0.001, OS: P < 0.001), tumor size (PFS: P = 0.044, OS: P = 0.04), TMZ therapy (PFS: P < 0.001, OS: P < 0.001), P53 (PFS: P < 0.001, OS: P < 0.001), and Ki67 (PFS: P < 0.001, OS: P < 0.001). In multivariate analysis, independent favorable prognostic factors for survival were pathology subtype (PFS: P < 0.001, OS: P < 0.001), type (PFS: P < 0.001, OS: 0.012), TMZ (PFS: P < 0.001, OS: P < 0.001), P53 (PFS: P < 0.001, OS: P < 0.001), and Ki67 (PFS: P < 0.001, OS: P < 0.001). The results suggest that pathology subtype, primary vs. secondary type, TMZ therapy, P53, and Ki 63 may play an important role in the survival of patients with glioblastoma multiforme. There is no relationship detected between age, gender, KPS, tumor size and location, necrosis size, extent of surgery, reoperation in recurrence, and patient survival.


Glioblastoma multiforme Prognostic factors Overall survival Progression-free survival Karnofsky performance status 



We appreciate the clinical Research Development Center of Imam Reza Hospital for their wise advice.

Authors’ contributions

EA and SRB had the idea for this study. EA and PR participated in outlining the concept and design. ZR and ASS did the data acquisition. EA and AA did the statistical analysis and wrote the first draft of the manuscript. EA, SRB, ASS, and AA revised the final manuscript. All authors have read and approved the manuscript.


There was no external source of funding.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study received ethics approval by the Kermanshah University of Medical Science Ethics Committee.

Informed consent

All participants provided informed consent prior to their participation.


  1. 1.
    D’Alessio A, Proietti G, Sica G, Scicchitano BM (2019) Pathological and molecular features of glioblastoma and its peritumoral tissue. Cancers 11(4):2–19Google Scholar
  2. 2.
    Eriksson M, Kahari J, Vestman A, Hallmans M, Johansson M, Bergenheim AT et al (2019) Improved treatment of glioblastoma—changes in survival over two decades at a single regional Centre. Acta oncologica (Stockholm, Sweden). 58(3):334–341CrossRefGoogle Scholar
  3. 3.
    Guden M, Ayata HB, Ceylan C, Kilic A, Engin K (2016) Prognostic factors effective on survival of patients with glioblastoma: Anadolu Medical Center experience. Indian J Cancer 53(3):382–386Google Scholar
  4. 4.
    Harat M, Blok M, Harat A, Soszynska K (2019) The impact of adjuvant radiotherapy on molecular prognostic markers in gliomas. OncoTargets Ther 12:2215–2224CrossRefGoogle Scholar
  5. 5.
    Chaudhry NS, Shah AH, Ferraro N, Snelling BM, Bregy A, Madhavan K et al (2013) Predictors of long-term survival in patients with glioblastoma multiforme: advancements from the last quarter century. Cancer Invest 31(5):287–308CrossRefGoogle Scholar
  6. 6.
    Myung JK, Cho HJ, Kim H, Park CK, Lee SH, Choi SH et al (2014) Prognosis of glioblastoma with oligodendroglioma component is associated with the IDH1 mutation and MGMT methylation status. Transl Oncol 7(6):712–719CrossRefGoogle Scholar
  7. 7.
    Roh TH, Kang SG, Moon JH, Sung KS, Park HH, Kim SH et al (2019) Survival benefit of lobectomy over gross-total resection without lobectomy in cases of glioblastoma in the noneloquent area: a retrospective study. J Neurosurg. Google Scholar
  8. 8.
    Rigamonti A, Imbesi F, Silvani A, Lamperti E, Agostoni E, Porcu L et al (2019) Prognostic nutritional index as a prognostic marker in glioblastoma: data from a cohort of 282 Italian patients. J Neurol Sci 400:175–179CrossRefGoogle Scholar
  9. 9.
    Smoll NR, Schaller K, Gautschi OP (2013) Long-term survival of patients with glioblastoma multiforme (GBM). J Clin Neurosci 20(5):670–675CrossRefGoogle Scholar
  10. 10.
    Piccolo SR, Frey LJ (2013) Clinical and molecular models of glioblastoma multiforme survival. Int J Data Min Bioinform 7(3):245–265CrossRefGoogle Scholar
  11. 11.
    Karsy M, Gelbman M, Shah P, Balumbu O, Moy F, Arslan E (2012) Established and emerging variants of glioblastoma multiforme: review of morphological and molecular features. Folia Neuropathol 50(4):301–321CrossRefGoogle Scholar
  12. 12.
    Woodworth GF, Garzon-Muvdi T, Ye X, Blakeley JO, Weingart JD, Burger PC (2013) Histopathological correlates with survival in reoperated glioblastomas. J Neurooncol 113(3):485–493CrossRefGoogle Scholar
  13. 13.
    Zhang LY, Ge HJ, Wang LM, Zhao LH, Liu L, Zhang DJ et al (2019) Prognostic implication of alterations in epidermal growth factor receptor and MGMT in glioblastoma. Zhonghua bing li xue za zhi Chinese J Pathol 48(3):186–191Google Scholar
  14. 14.
    Sales AHA, Bette S, Barz M, Huber T, Wiestler B, Ryang YM et al (2019) Role of postoperative tumor volume in patients with MGMT-unmethylated glioblastoma. J Neuro-oncol 142(3):529–536CrossRefGoogle Scholar
  15. 15.
    Alexiou GA, Vartholomatos E, Tsamis KI, Peponi E, Markopoulos G, Papathanasopoulu VA et al (2019) Combination treatment for glioblastoma with temozolomide, DFMO and radiation. J BUON 24(1):397–404Google Scholar
  16. 16.
    Henker C, Kriesen T, Schneider B, Glass A, Scherer M, Langner S et al (2019) Correlation of Ki-67 index with volumetric segmentation and its value as a prognostic marker in glioblastoma. World Neurosurg 125:e1093–e1103CrossRefGoogle Scholar
  17. 17.
    Syed M, Liermann J, Verma V, Bernhardt D, Bougatf N, Paul A et al (2018) Survival and recurrence patterns of multifocal glioblastoma after radiation therapy. Cancer Manag Res 10:4229–4235CrossRefGoogle Scholar
  18. 18.
    Wang X, Liu YH, Xie F, You C, Mao Q (2013) A clinical and molecular study of long-term survival glioblastomas. Zhonghua wai ke za zhi [Chin J Surg] 51(2):166–170Google Scholar
  19. 19.
    Li WB, Tang K, Chen Q, Li S, Qiu XG, Li SW et al (2012) MRI manifestions correlate with survival of glioblastoma multiforme patients. Cancer Biol Med 9(2):120–123Google Scholar
  20. 20.
    Delgado-Lopez PD, Corrales-Garcia EM (2016) Survival in glioblastoma: a review on the impact of treatment modalities. Clin Transl Oncol 18(11):1062–1071CrossRefGoogle Scholar
  21. 21.
    De Barros A, Attal J, Roques M, Nicolau J, Sol JC, Cohen-Jonathan-Moyal E et al (2019) Impact on survival of early tumor growth between surgery and radiotherapy in patients with de novo glioblastoma. J Neuro-oncol 142(3):489–497CrossRefGoogle Scholar
  22. 22.
    Chaichana KL, Martinez-Gutierrez JC, De la Garza-Ramos R, Weingart JD, Olivi A, Gallia GL et al (2013) Factors associated with survival for patients with glioblastoma with poor pre-operative functional status. J Clin Neurosci 20(6):818–823CrossRefGoogle Scholar
  23. 23.
    Byun J, Kim YH, Nam SJ, Park JE, Cho YH, Kim HS et al (2019) Comparison of survival outcomes between partial resection and biopsy for primary glioblastoma: a propensity score-matched study. World Neurosurg 121:e858–e866CrossRefGoogle Scholar
  24. 24.
    Back M, Jayamanne D, Cove N, Wheeler H, Khasraw M, Guo L et al (2018) Optimising outcomes for glioblastoma through subspecialisation in a regional cancer centre. Brain Sci 8(10):186CrossRefGoogle Scholar
  25. 25.
    Ahmadipour Y, Jabbarli R, Gembruch O, Pierscianek D, Darkwah Oppong M, Dammann P et al (2019) Impact of multifocality and molecular markers on survival of glioblastoma. World Neurosurg 122:e461–e466CrossRefGoogle Scholar
  26. 26.
    Palmer JD, Bhamidipati D, Shukla G, Sharma D, Glass J, Kim L et al (2019) Rapid early tumor progression is prognostic in glioblastoma patients. Am J Clin Oncol 42(5):481–486CrossRefGoogle Scholar

Copyright information

© Belgian Neurological Society 2019
corrected publication July 2019

Authors and Affiliations

  1. 1.Imam Reza HospitalKermanshah University of Medical SciencesKermanshahIran

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