Clinical Prognostic Factors Predicting Outcomes Of Patients With Recurrent Gbm And Nomograms

Détails

ID Serval
serval:BIB_D6E724A21EE2
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Sous-type
Abstract (résumé de présentation): article court qui reprend les éléments essentiels présentés à l'occasion d'une conférence scientifique dans un poster ou lors d'une intervention orale.
Collection
Publications
Institution
Titre
Clinical Prognostic Factors Predicting Outcomes Of Patients With Recurrent Gbm And Nomograms
Titre de la conférence
15th Annual Meeting of the Society for Neuro-Oncology (SNO)
Auteur(s)
Gorlia T., Brandes A., Stupp R., Rampling R., Fumoleau P., Dittrich C., Campone M., Twelves C., Raymond E., Lacombe D., Van Den Bent M.J.
Adresse
Montreal, Canada, November 18-21, 2010
ISBN
1522-8517
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Volume
12
Série
Neuro-Oncology
Pages
67-67
Langue
anglais
Notes
Publication type : Meeting Abstract
Résumé
BACKGROUND: Prognostic models and nomograms were recently developed to predict survival of patients with newly diagnosed glioblastoma multiforme (GBM).1 To improve predictions, models should be updated with the most recent patient and disease information. Nomograms predicting patient outcome at the time of disease progression are required. METHODS: Baseline information from 299 patients with recurrent GBM recruited in 8 phase I or II trials of the EORTC Brain Tumor Group was used to evaluate clinical parameters as prognosticators of patient outcome. Univariate (log rank) and multivariate (Cox models) analyses were made to assess the ability of patients' characteristics (age, sex, performance status [WHO PS], and MRC neurological deficit scale), disease history (prior treatments, time since last treatment or initial diagnosis, and administration of steroids or antiepileptics) and disease characteristics (tumor size and number of lesions) to predict progression free survival (PFS) and overall survival (OS). Bootstrap technique was used for models internal validation. Nomograms were computed to provide individual patients predictions. RESULTS: Poor PS and more than 1 lesion had a significant prognostic impact for both PFS and OS. Antiepileptic drug use was significantly associated with worse PFS. Larger tumors (split by the median of the largest tumor diameter >42.5 mm) and steroid use had shorter OS. Age, sex, neurologic deficit, prior therapies, and time since last therapy or initial diagnosis did not show independent prognostic value for PFS or OS. CONCLUSIONS: This analysis confirms that PS but not age is a major prognostic factor for PFS and OS. Multiple or large tumors and the need to administer steroids significantly increase the risk of progression and death. Nomograms at the recurrence could be used to obtain accurate predictions for the design of new targeted therapy trials or retrospective analyses. (1. T. Gorlia et al., Nomograms for predicting survival of patients with newly diagnosed glioblastoma. Lancet Oncol 9 (1): 29-38, 2008.)
Mots-clé
,
Web of science
Création de la notice
23/02/2011 9:25
Dernière modification de la notice
20/08/2019 15:56
Données d'usage