Title

Recursive Partitioning Analysis of Prognostic Variables in Newly Diagnosed Anaplastic Oligodendroglial Tumors

Department

neurology

Document Type

Article

Abstract

Background: Anaplastic oligodendroglial tumors are rare, and median survival varies widely. Analysis of 1p19q deletion is performed commonly and is an important prognostic factor. However, age and other clinical variables also carry prognostic value, and it is unclear how to incorporate them into clinical decision making or to combine them for prognostication. Methods. We compiled a retrospective database of 1013 patients with newly diagnosed anaplastic oligodendrogliomas or oligoastrocytomas and performed a recursive partitioning analysis to generate independent prognostic classes among 587 patients with informative 1p19q status. Variables included for survival classification were age (continuous), history of prior low-grade glioma, 1p19q deletion status, histology (presence or absence of an astrocytic component), tumor lobe, tumor hemisphere, gender, extent of resection, postresection treatment, and performance status at diagnosis. Results. Recursive partitioning analysis identified 5 prognostic groups based on hazard similarity: class I (age, 60 y, 1p19q codeleted), class II (age<43 y, not codeleted), class III (age 43-59 y, not codeleted, frontal lobe tumor or age ≥60 y, codeleted), class IV (age 43-59 y, not codeleted, not frontal lobe tumor or age 60-69 y, not codeleted), and class V (age ≥70 y, not codeleted). Survival differenceswere highly significant (P<.0001), with medians ranging from 9.3 years (95% CI: 8.4-16.0) for class I to 0.6 years (95% CI: 0.5-0.9) for class V. Conclusions. These 5 distinct classification groups were defined using prognostic factors typically obtained during routine management of patients with anaplastic oligodendroglial tumors. Validation in a prospective clinical trial may better differentiate patients with respect to treatment outcome.

Medical Subject Headings

neurology

Publication Date

2014

Publication Title

Neuro-Oncology

ISSN

15228517

Volume

16

Issue

11

First Page

1541

Last Page

1546

Digital Object Identifier (DOI)

10.1093/neuonc/nou083

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