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J Neurooncol. 2018 Feb;136(3):565-576. doi: 10.1007/s11060-017-2685-4. Epub 2017 Nov 20.

Prognostic factors for survival in adult patients with recurrent glioblastoma: a decision-tree-based model.

Author information

1
Public Health Department, Henri Mondor Teaching Hospital, Créteil, France.
2
Laboratoire d'Investigation Clinique, EA 4393, Université Paris Est Créteil, Créteil, France.
3
Department of Neurosurgery, Sainte-Anne Hospital, Paris, France.
4
Paris Descartes University, Paris, France.
5
Department of Neurology, Avicenne Hospital, AP-HP, Bobigny, France.
6
Department of Neurosurgery, Leeds General Infirmary, Leeds, UK.
7
Department of Neurosurgery, Clairval Private Hospital, Marseille, France.
8
UMR911, CRO2, Aix-Marseille Université, Marseille, France.
9
Department of Radiotherapy, Centre de Lutte Contre le Cancer Paul Strauss, Strasbourg, France.
10
Radiobiology laboratory, EA 3440, Federation of Translational Medicine de Strasbourg (FMTS), Strasbourg University, Strasbourg, France.
11
Department of Neurosurgery, University Hospital of Montpellier, Montpellier, France.
12
Service of Neurosurgery D, Lyon Civil Hospitals, Pierre Wertheimer Neurological and Neurosurgical Hospital, Lyon, France.
13
Department of Neurosurgery, University Hospital Pontchaillou, Rennes, France.
14
Department of Neurosurgery, APHP Beaujon Hospital, Clichy, France.
15
Department of Neurosurgery, Maison Blanche Hospital, Reims University Hospital, Reims, France.
16
Department of Neurosurgery, Sainte Anne Military Teaching Hospital, Toulon, France.
17
Department of Neurosurgery, University Hospital Jean Minjoz, Besançon, France.
18
Departement of Neurosurgery, University Hospital of Caen, University of Lower Normandy, Caen, France.
19
Department of Pathology, Caen University Hospital, Caen, France.
20
CNRS, UMR 6232 CERVOxy Group, Caen, France.
21
University of Caen Basse-Normandie, UMR 6232 CERVOxy Group, Caen, France.
22
CEA, UMR 6232 CERVOxy Group, Caen, France.
23
Department of Neurosurgery, Amiens University Hospital, Amiens, France.
24
Department of Neurosurgery, Pasteur Hospital, Colmar, France.
25
Department of Neurosurgery, CHU d'Angers, Angers, France.
26
Service de Neurochirurgie, CHU de Limoges, Limoges, France.
27
Department of Neurosurgery, Faculty of Medicine, University Medical Centre, University of Brest, Brest, France.
28
Department of Neurosurgery, Rouen University Hospital, Rouen, France.
29
Service de Neurochirurgie A, CHU Pellegrin, Bordeaux Cedex, France.
30
Department of Biostatistique, Epidémiologie clinique, Santé Publique, Informations Médicales, University Hospital of Nîmes, Nîmes, France.
31
Inserm, U894, IMABRAIN, Centre Psychiatrie et Neurosciences, Paris, France.
32
Inserm, U1051, Montpellier, France.
33
Department of Neurosurgery, Sainte-Anne Hospital, Paris, France. johanpallud@hotmail.com.
34
Paris Descartes University, Paris, France. johanpallud@hotmail.com.
35
Inserm, U894, IMABRAIN, Centre Psychiatrie et Neurosciences, Paris, France. johanpallud@hotmail.com.

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.

KEYWORDS:

Conditional random forest; Cox model; Decision tree; Glioblastoma; Karnofsky performance status; Overall survival; Prognostic models; Random survival forest; Recurrence; Recursive partitioning analysis; Surgery

PMID:
29159777
DOI:
10.1007/s11060-017-2685-4
[Indexed for MEDLINE]

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