Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older Journal Article


Authors: 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-Daude, H.; Fabbro-Peray, P.; Rigau, V.; Taillandier, L.; Abrey, L. E.; De Angelis, L. M.; Shih, J. H.; Iwamoto, F. M.
Article Title: Recursive partitioning analysis of prognostic factors for glioblastoma patients aged 70 years or older
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. © 2012 American Cancer Society.
Keywords: cancer survival; human tissue; aged; aged, 80 and over; cancer surgery; survival rate; major clinical study; follow up; brain neoplasms; analytic method; cohort analysis; tumor biopsy; retrospective study; karnofsky performance status; glioblastoma; surgery; aging; elderly; recursive partitioning analysis; cancer prognosis
Journal Title: Cancer
Volume: 118
Issue: 22
ISSN: 0008-543X
Publisher: Wiley Blackwell  
Date Published: 2012-11-15
Start Page: 5595
End Page: 5600
Language: English
DOI: 10.1002/cncr.27570
PROVIDER: scopus
PMCID: PMC3402652
PUBMED: 22517216
DOI/URL:
Notes: --- - "Export Date: 3 December 2012" - "CODEN: CANCA" - "Source: Scopus"
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MSK Authors
  1. Lakshmi Nayak
    18 Nayak
  2. Lauren E Abrey
    272 Abrey