| Literature DB >> 29159777 |
Etienne Audureau1,2, Anaïs Chivet3,4, Renata Ursu5, Robert Corns6, Philippe Metellus7,8, Georges Noel9,10, Sonia Zouaoui11, Jacques Guyotat12, Pierre-Jean Le Reste13, Thierry Faillot14, Fabien Litre15, Nicolas Desse16, Antoine Petit17, Evelyne Emery18, Emmanuelle Lechapt-Zalcman19,20,21,22, Johann Peltier23, Julien Duntze15, Edouard Dezamis1,2, Jimmy Voirin24, Philippe Menei25, François Caire26, Phong Dam Hieu27, Jean-Luc Barat6, Olivier Langlois28, Jean-Rodolphe Vignes29, Pascale Fabbro-Peray30, Adeline Riondel30, Elodie Sorbets30, Marc Zanello1,2, Alexandre Roux1,2,31, Antoine Carpentier5, Luc Bauchet11,32, Johan Pallud33,34,35.
Abstract
We assessed prognostic factors in relation to OS from progression in recurrent glioblastomas. Retrospective multicentric study enrolling 407 (training set) and 370 (external validation set) adult patients with a recurrent supratentorial glioblastoma treated by surgical resection and standard combined chemoradiotherapy as first-line treatment. Four complementary multivariate prognostic models were evaluated: Cox proportional hazards regression modeling, single-tree recursive partitioning, random survival forest, conditional random forest. Median overall survival from progression was 7.6 months (mean, 10.1; range, 0-86) and 8.0 months (mean, 8.5; range, 0-56) in the training and validation sets, respectively (p = 0.900). Using the Cox model in the training set, independent predictors of poorer overall survival from progression included increasing age at histopathological diagnosis (aHR, 1.47; 95% CI [1.03-2.08]; p = 0.032), RTOG-RPA V-VI classes (aHR, 1.38; 95% CI [1.11-1.73]; p = 0.004), decreasing KPS at progression (aHR, 3.46; 95% CI [2.10-5.72]; p < 0.001), while independent predictors of longer overall survival from progression included surgical resection (aHR, 0.57; 95% CI [0.44-0.73]; p < 0.001) and chemotherapy (aHR, 0.41; 95% CI [0.31-0.55]; p < 0.001). Single-tree recursive partitioning identified KPS at progression, surgical resection at progression, chemotherapy at progression, and RTOG-RPA class at histopathological diagnosis, as main survival predictors in the training set, yielding four risk categories highly predictive of overall survival from progression both in training (p < 0.0001) and validation (p < 0.0001) sets. Both random forest approaches identified KPS at progression as the most important survival predictor. Age, KPS at progression, RTOG-RPA classes, surgical resection at progression and chemotherapy at progression are prognostic for survival in recurrent glioblastomas and should inform the treatment decisions.Entities:
Keywords: Conditional random forest; Cox model; Decision tree; Glioblastoma; Karnofsky performance status; Overall survival; Prognostic models; Random survival forest; Recurrence; Recursive partitioning analysis; Surgery
Mesh：
Year: 2017 PMID： 29159777 DOI： 10.1007/s11060-017-2685-4
Source DB: PubMed Journal: J Neurooncol ISSN： 0167-594X Impact factor: 4.130