Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older |
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| Authors: | Tyler J. Fraum MD Lakshmi Nayak MD Anna R. Cooper MD Samuel T. Chao MD John H. Suh MD Michael A. Vogelbaum MD PhD David M. Peereboom MD Sonia Zouaoui PhD Hélène Mathieu‐Daudé MD Pascale Fabbro‐Peray MD PhD Valérie Rigau MD PhD Luc Taillandier MD PhD Lauren E. Abrey MD Lisa M. DeAngelis MD Joanna H. Shih PhD Fabio M. Iwamoto MD |
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| Affiliation: | 1. Neuro‐Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland;2. Duke University School of Medicine, Durham, North Carolina;3. Brain Tumor Center and Department of Neurology, Memorial Sloan‐Kettering Cancer Center, New York, New York;4. Department of Orthopedics at the University of Rochester Medical Center, Rochester, New York;5. Cleveland Clinic Brain Tumor Institute, Cleveland, Ohio;6. Department of Neurosurgery and Institut National de la Santé et de la Recherche Médicale (INSERM) U1051, H?pital Saint Eloi–Gui de Chauliac, Centre Hospitalier Universitaire, Montpellier, France;7. 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;8. Department of Biostatistics, Institut Universitaire de Recherche Clinique, Montpellier, France;9. Biostatistique, Epidémiologie clinique, Santé Publique et Information Médicale (BESPIM), Centre Hospitalier Universitaire, N?mes, France;10. Department of Pathology, Centre Hospitalier Universitaire, H?pital Gui de Chauliac, Montpellier, France;11. Department of Neuro‐Oncology, H?pital Neurologique, Nancy, France;12. Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland;13. Neuro‐Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MarylandFax: (301) 480‐1259 |
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| 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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| Keywords: | glioblastoma elderly surgery prognosis aging |
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