Development and external validation of a clinical prediction model for survival in patients with IDH wild-type glioblastoma

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  • 1 Departments of Neurosurgery and
  • | 2 Neuropathology, Heinrich Heine University, Medical Faculty, Düsseldorf, Germany;
  • | 3 Departments of Public Health and
  • | 4 Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam;
  • | 5 Departments of Neurosurgery and
  • | 6 Neurology, Brain Tumor Centre, Erasmus MC Cancer Institute, University Medical Center, Rotterdam;
  • | 7 Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands;
  • | 8 Department of Neurosurgery, and
  • | 9 Dr. Senckenberg Institute of Neurooncology, Goethe University, Medical Faculty, Frankfurt; and
  • | 10 Department of Neurosurgery, Friedrich Schiller University, Medical Faculty, Jena, Germany
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OBJECTIVE

Prognostication of glioblastoma survival has become more refined due to the molecular reclassification of these tumors into isocitrate dehydrogenase (IDH) wild-type and IDH mutant. Since this molecular stratification, however, robust clinical prediction models relevant to the entire IDH wild-type glioblastoma patient population are lacking. This study aimed to provide an updated model that predicts individual survival prognosis in patients with IDH wild-type glioblastoma.

METHODS

Databases from Germany and the Netherlands provided data on 1036 newly diagnosed glioblastoma patients treated between 2012 and 2018. A clinical prediction model for all-cause mortality was developed with Cox proportional hazards regression. This model included recent glioblastoma-associated molecular markers in addition to well-known classic prognostic variables, which were updated and refined with additional categories. Model performance was evaluated according to calibration (using calibration plots and calibration slope) and discrimination (using a C-statistic) in a cross-validation procedure by country to assess external validity.

RESULTS

The German and Dutch patient cohorts consisted of 710 and 326 patients, respectively, of whom 511 (72%) and 308 (95%) had died. Three models were developed, each with increasing complexity. The final model considering age, sex, preoperative Karnofsky Performance Status, extent of resection, O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status, and adjuvant therapeutic regimen showed an optimism-corrected C-statistic of 0.73 (95% confidence interval 0.71–0.75). Cross-validation between the national cohorts yielded comparable results.

CONCLUSIONS

This prediction model reliably predicts individual survival prognosis in patients with newly diagnosed IDH wild-type glioblastoma, although additional validation, especially for long-term survival, may be desired. The nomogram and web application of this model may support shared decision-making if used properly.

ABBREVIATIONS

CI = confidence interval; EOR = extent of resection; GTR = gross-total resection; HR = hazard ratio; IDH = isocitrate dehydrogenase; KPS = Karnofsky Performance Status.

Supplementary Materials

    • Supplemental Tables and Figure (PDF 1,163 KB)

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  • 1

    Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011-2015. Neuro Oncol. 2018;20(suppl 4):iv1iv86.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al. 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. 2009;10(5):459466.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3

    Molinaro AM, Taylor JW, Wiencke JK, Wrensch MR. Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol. 2019;15(7):405417.

  • 4

    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 2016;131(6):803820.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987996.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):9971003.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7

    Gorlia T, van den Bent MJ, Hegi ME, Mirimanoff RO, Weller M, Cairncross JG, et al. Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981-22981/CE.3. Lancet Oncol. 2008;9(1):2938.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Gessler F, Bernstock JD, Braczynski A, Lescher S, Baumgarten P, Harter PN, et al. Surgery for glioblastoma in light of molecular markers: impact of resection and MGMT promoter methylation in newly diagnosed IDH-1 wild-type glioblastomas. Neurosurgery. 2019;84(1):190197.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Gittleman H, Cioffi G, Chunduru P, Molinaro AM, Berger MS, Sloan AE, Barnholtz-Sloan JS. An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival. Neurooncol Adv. 2019;1(1):vdz007.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Molinaro AM, Hervey-Jumper S, Morshed RA, Young J, Han SJ, Chunduru P, et al. Association of maximal extent of resection of contrast-enhanced and non–contrast-enhanced tumor with survival within molecular subgroups of patients with newly diagnosed glioblastoma. JAMA Oncol. 2020;6(4):495503.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11

    Shen E, Johnson MO, Lee JW, Lipp ES, Randazzo DM, Desjardins A, et al. Performance of a nomogram for IDH-wild-type glioblastoma patient survival in an elderly cohort. Neurooncol Adv. 2019;1(1):vdz036.

    • Search Google Scholar
    • Export Citation
  • 12

    Felsberg J, Wolter M, Seul H, Friedensdorf B, Göppert M, Sabel MC, Reifenberger G. Rapid and sensitive assessment of the IDH1 and IDH2 mutation status in cerebral gliomas based on DNA pyrosequencing. Acta Neuropathol. 2010;119(4):501507.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Hartmann C, Hentschel B, Wick W, Capper D, Felsberg J, Simon M, et al. Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol. 2010;120(6):707718.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Incekara F, Smits M, van der Voort SR, Dubbink HJ, Atmodimedjo PN, Kros JM, et al. The association between the extent of glioblastoma resection and survival in light of MGMT promoter methylation in 326 patients with newly diagnosed IDH-wildtype glioblastoma. Front Oncol. 2020;10 1087.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Felsberg J, Rapp M, Loeser S, Fimmers R, Stummer W, Goeppert M, et al. Prognostic significance of molecular markers and extent of resection in primary glioblastoma patients. Clin Cancer Res. 2009;15(21):66836693.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16

    Felsberg J, Thon N, Eigenbrod S, Hentschel B, Sabel MC, Westphal M, et al. Promoter methylation and expression of MGMT and the DNA mismatch repair genes MLH1, MSH2, MSH6 and PMS2 in paired primary and recurrent glioblastomas. Int J Cancer. 2011;129(3):659670.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Mijderwijk HJ, Steyerberg EW, Steiger HJ, Fischer I, Kamp MA. Fundamentals of clinical prediction modeling for the neurosurgeon. Neurosurgery. 2019;85(3):302311.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Riley RD, Ensor J, Snell KIE, Harrell FE Jr, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368 m441.

  • 19

    Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer International Publishing;2019.

    • Search Google Scholar
    • Export Citation
  • 20

    Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med. 2015;13 1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21

    Heus P, Reitsma JB, Collins GS, Damen JAAG, Scholten RJPM, Altman DG, et al. Transparent reporting of multivariable prediction models in journal and conference abstracts: TRIPOD for abstracts. Ann Intern Med. 2020;173(42):47.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22

    Collins GS, Ogundimu EO, Cook JA, Manach YL, Altman DG. Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model. Stat Med. 2016;35(23):41244135.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Harrell FE Jr, Lee KL, Pollock BG. Regression models in clinical studies: determining relationships between predictors and response. J Natl Cancer Inst. 1988;80(15):11981202.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24

    Van Buuren S. Flexible Imputation of Missing Data. Chapman & Hall/CRC;2018.

  • 25

    Rubin DB. Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons;1987.

  • 26

    Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol. 2013;13 33.

  • 27

    Riley RD, Ensor J, Snell KIE, Debray TP, Altman DG, Moons KG, Collins GS. External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ. 2016;353 i3140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28

    Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA. 1982;247(18):25432546.

  • 29

    Vergouwe Y, Moons KGM, Steyerberg EW. External validity of risk models: use of benchmark values to disentangle a case-mix effect from incorrect coefficients. Am J Epidemiol. 2010;172(8):971980.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Steyerberg EW, Harrell FE Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol. 2016;69(245):247.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31

    Gittleman H, Lim D, Kattan MW, Chakravarti A, Gilbert MR, Lassman AB, et al. An independently validated nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma: NRG Oncology RTOG 0525 and 0825. Neuro Oncol. 2017;19(5):669677.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32

    Loughan AR, Lanoye A, Aslanzadeh FJ, Villanueva AAL, Boutte R, Husain M, Braun S. Fear of cancer recurrence and death anxiety: unaddressed concerns for adult neuro-oncology patients. J Clin Psychol Med Settings. 2021;28(1):1630.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33

    Braun M, Mikulincer M, Rydall A, Walsh A, Rodin G. Hidden morbidity in cancer: spouse caregivers. J Clin Oncol. 2007;25(30):48294834.

  • 34

    Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, et al. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469474.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Stupp R, Taillibert S, Kanner A, Read W, Steinberg D, Lhermitte B, et al. Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: a randomized clinical trial. JAMA. 2017;318(23):23062316.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Tan AC, Ashley DM, López GY, Malinzak M, Friedman HS, Khasraw M. Management of glioblastoma: state of the art and future directions. CA Cancer J Clin. 2020;70(4):299312.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37

    Pitter KL, Tamagno I, Alikhanyan K, Hosni-Ahmed A, Pattwell SS, Donnola S, et al. Corticosteroids compromise survival in glioblastoma. Brain. 2016;139(Pt 5):14581471.

  • 38

    van Breemen MS, Rijsman RM, Taphoorn MJB, Walchenbach R, Zwinkels H, Vecht CJ. Efficacy of anti-epileptic drugs in patients with gliomas and seizures. J Neurol. 2009;256(9):15191526.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 39

    Perry JR. Thromboembolic disease in patients with high-grade glioma. Neuro Oncol. 2012;14(suppl 4):iv73iv80.

  • 40

    Gravesteijn BY, Nieboer D, Ercole A, Lingsma HF, Nelson D, van Calster B, Steyerberg EW. Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol. 2020;122(95):107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 41

    van der Ploeg T, Nieboer D, Steyerberg EW. Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury. J Clin Epidemiol. 2016;78(83):89.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 42

    van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014;14 137.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

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