title: A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.
abstract: OBJECTIVE
  Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM.
  
  
  METHODS
  The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T1weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal-Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan-Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM.
  
  
  RESULTS
  Our analysis using the Kruskal-Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm-Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan-Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value < 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival.
  
  
  CONCLUSIONS
  Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM. Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM.
authors: 1 Laboratory for Imagery, Vision and Artificial Intelligence, University of Québec, École de Technologie Supérieure, Montréal, QC, Canada.|2 Laboratory of Conception, Optimization and Modeling of Systems, University of Lorraine, Metz, Lorraine, France.
authors: Ahmad
authors: A
authors: Chaddad
authors: 1 Laboratory for Imagery, Vision and Artificial Intelligence, University of Québec, École de Technologie Supérieure, Montréal, QC, Canada.
authors: Christian
authors: C
authors: Desrosiers
authors: 1 Laboratory for Imagery, Vision and Artificial Intelligence, University of Québec, École de Technologie Supérieure, Montréal, QC, Canada.|2 Laboratory of Conception, Optimization and Modeling of Systems, University of Lorraine, Metz, Lorraine, France.
authors: Lama
authors: L
authors: Hassan
authors: 2 Laboratory of Conception, Optimization and Modeling of Systems, University of Lorraine, Metz, Lorraine, France.
authors: Camel
authors: C
authors: Tanougast
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001921:Brain / Q000000981:diagnostic imaging; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D005069:Evaluation Studies as Topic; D005260:Female; D005909:Glioblastoma / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D015203:Reproducibility of Results; D016019:Survival Analysis; D055815:Young Adult