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.
authors.affiliation: Department of Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida, USA.
authors.forename: Jacob G
authors.initials: JG
authors.lastname: Scott
authors.forename: Luc
authors.initials: L
authors.lastname: Bauchet
authors.forename: Tyler J
authors.initials: TJ
authors.lastname: Fraum
authors.forename: Lakshmi
authors.initials: L
authors.lastname: Nayak
authors.forename: Anna R
authors.initials: AR
authors.lastname: Cooper
authors.forename: Samuel T
authors.initials: ST
authors.lastname: Chao
authors.forename: John H
authors.initials: JH
authors.lastname: Suh
authors.forename: Michael A
authors.initials: MA
authors.lastname: Vogelbaum
authors.forename: David M
authors.initials: DM
authors.lastname: Peereboom
authors.forename: Sonia
authors.initials: S
authors.lastname: Zouaoui
authors.forename: Hélène
authors.initials: H
authors.lastname: Mathieu-Daudé
authors.forename: Pascale
authors.initials: P
authors.lastname: Fabbro-Peray
authors.forename: Valérie
authors.initials: V
authors.lastname: Rigau
authors.forename: Luc
authors.initials: L
authors.lastname: Taillandier
authors.forename: Lauren E
authors.initials: LE
authors.lastname: Abrey
authors.forename: Lisa M
authors.initials: LM
authors.lastname: DeAngelis
authors.forename: Joanna H
authors.initials: JH
authors.lastname: Shih
authors.forename: Fabio M
authors.initials: FM
authors.lastname: Iwamoto
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000401:mortality* / Q000601:surgery*; D005260:Female; D005909:Glioblastoma / Q000401:mortality* / Q000601:surgery*; D006801:Humans; D017567:Karnofsky Performance Status; D008297:Male; D011379:Prognosis; D015996:Survival Rate