Original Article

Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older†‡

  1. Jacob G. Scott MD1,§,
  2. Luc Bauchet MD, PhD2,§,
  3. Tyler J. Fraum MD3,4,
  4. Lakshmi Nayak MD5,
  5. Anna R. Cooper MD6,
  6. Samuel T. Chao MD7,
  7. John H. Suh MD7,
  8. Michael A. Vogelbaum MD, PhD7,
  9. David M. Peereboom MD7,
  10. Sonia Zouaoui PhD2,
  11. Hélène Mathieu-Daudé MD8,
  12. Pascale Fabbro-Peray MD, PhD9,10,
  13. Valérie Rigau MD, PhD11,
  14. Luc Taillandier MD, PhD12,
  15. Lauren E. Abrey MD5,
  16. Lisa M. DeAngelis MD5,
  17. Joanna H. Shih PhD13,
  18. Fabio M. Iwamoto MD3,¶,*

Article first published online: 19 APR 2012

DOI: 10.1002/cncr.27570

Issue

Cancer

Cancer

Volume 118, Issue 22, pages 5595–5600, 15 November 2012

Additional Information

How to Cite

Scott, J. G., Bauchet, L., Fraum, T. J., Nayak, L., Cooper, A. R., Chao, S. T., Suh, J. H., Vogelbaum, M. A., Peereboom, D. M., Zouaoui, S., Mathieu-Daudé, H., Fabbro-Peray, P., Rigau, V., Taillandier, L., Abrey, L. E., DeAngelis, L. M., Shih, J. H. and Iwamoto, F. M. (2012), Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older. Cancer, 118: 5595–5600. doi: 10.1002/cncr.27570

Author Information

  1. 1

    Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida

  2. 2

    Department of Neurosurgery and Institut National de la Santé et de la Recherche Médicale (INSERM) U1051, Hôpital Saint Eloi–Gui de Chauliac, Centre Hospitalier Universitaire, Montpellier, France

  3. 3

    Neuro-Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland

  4. 4

    Duke University School of Medicine, Durham, North Carolina

  5. 5

    Brain Tumor Center and Department of Neurology, Memorial Sloan-Kettering Cancer Center, New York, New York

  6. 6

    Department of Orthopedics at the University of Rochester Medical Center, Rochester, New York

  7. 7

    Cleveland Clinic Brain Tumor Institute, Cleveland, Ohio

  8. 8

    Department of Epidemiology, Neuro-Oncology Group of Languedoc-Roussillon, Registre des Tumeurs de l'Hérault, Centre de Lutte Contre le Cancer Val d'Aurelle, Montpellier, France

  9. 9

    Department of Biostatistics, Institut Universitaire de Recherche Clinique, Montpellier, France

  10. 10

    Biostatistique, Epidémiologie clinique, Santé Publique et Information Médicale (BESPIM), Centre Hospitalier Universitaire, Nîmes, France

  11. 11

    Department of Pathology, Centre Hospitalier Universitaire, Hôpital Gui de Chauliac, Montpellier, France

  12. 12

    Department of Neuro-Oncology, Hôpital Neurologique, Nancy, France

  13. 13

    Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland

  1. §

    The first 3 authors contributed equally to this manuscript.

  2. Fax: (301) 480-1259

*Neuro-Oncology Branch, National Institutes of Health, 9030 Old Georgetown Road, Room 221, Bethesda, MD 20892-8202

  1. †

    The first 3 authors contributed equally to this manuscript.

  2. ‡

    This study was presented in part at the 2010 Annual Meeting of the American Society of Clinical Oncology; June 4-8, 2010; Chicago, Illinois.

  3. §

    The first 3 authors contributed equally to this manuscript.

  4. Fax: (301) 480-1259

Publication History

  1. Issue published online: 30 OCT 2012
  2. Article first published online: 19 APR 2012
  3. Manuscript Accepted: 13 FEB 2012
  4. Manuscript Revised: 6 FEB 2012
  5. Manuscript Received: 6 DEC 2011

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Keywords:

  • glioblastoma;
  • elderly;
  • surgery;
  • prognosis;
  • aging

Abstract

BACKGROUND:

The most-used prognostic scheme for malignant gliomas included only patients aged 18 to 70 years. The purpose of this study was to develop a prognostic model for patients ≥70 years of age with newly diagnosed glioblastoma.

METHODS:

A total of 437 patients ≥70 years of age with newly diagnosed glioblastoma, pooled from 2 tertiary academic institutions, was identified for recursive partitioning analysis (RPA). The resulting prognostic model, based on the final pruned RPA tree, was validated using 265 glioblastoma patients ≥70 years of age from a data set independently compiled by a French consortium.

RESULTS:

RPA produced 9 terminal nodes, which were pruned to 4 prognostic subgroups with markedly different median survivals: subgroup I = patients <75.5 years of age who underwent surgical resection (9.3 months); subgroup II = patients ≥75.5 years of age who underwent surgical resection (6.4 months); subgroup III = patients with Karnofsky performance status of 70 to 100 who underwent biopsy only (4.6 months); and subgroup IV = patients with Karnofsky performance status <70 who underwent biopsy only (2.3 months). Application of this prognostic model to the French cohort also resulted in significantly different (P < .0001) median survivals for subgroups I (8.5 months), II (7.7 months), III (4.3 months), and IV (3.1 months).

CONCLUSIONS:

This model divides elderly glioblastoma patients into prognostic subgroups that can be easily implemented in both the patient care and the clinical trial settings. This purely clinical prognostic model serves as a backbone for the future incorporation of the increasing number of potential molecular prognostic markers. Cancer 2012. © 2012 American Cancer Society.

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