title: Imaging descriptors improve the predictive power of survival models for glioblastoma patients.
abstract: BACKGROUND
  Because effective prediction of survival time can be highly beneficial for the treatment of glioblastoma patients, the relationship between survival time and multiple patient characteristics has been investigated. In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model.
  
  
  METHODS
  The subjects in this study were 82 glioblastoma patients for whom clinical features as well as MR imaging exams were made available by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). Twenty-six imaging features in the available MR scans were assessed by radiologists from the TCGA Glioma Phenotype Research Group. We used multivariate Cox proportional hazards regression to construct 2 survival models: one that used 3 clinical features (age, gender, and KPS) as the covariates and 1 that used both the imaging features and the clinical features as the covariates. Then, we used 2 measures to compare the predictive performance of these 2 models: area under the receiver operating characteristic curve for the 1-year survival threshold and overall concordance index. To eliminate any positive performance estimation bias, we used leave-one-out cross-validation.
  
  
  RESULTS
  The performance of the model based on both clinical and imaging features was higher than the performance of the model based on only the clinical features, in terms of both area under the receiver operating characteristic curve (P < .01) and the overall concordance index (P < .01).
  
  
  CONCLUSIONS
  Imaging features assessed using a controlled lexicon have additional predictive value compared with clinical features when predicting survival time in glioblastoma patients.
authors: Corresponding Author: Jordan Malof, PhD, Department of Electrical & Computer Engineering, Duke University, 130 Hudson Hall, Durham, NC 27708. jordan.malof@duke.edu.
authors: Maciej Andrzej
authors: MA
authors: Mazurowski
authors: Annick
authors: A
authors: Desjardins
authors: Jordan Milton
authors: JM
authors: Malof
mesh_terms: D001932:Brain Neoplasms / Q000175:diagnosis* / Q000401:mortality* / Q000628:therapy; D005909:Glioblastoma / Q000175:diagnosis* / Q000401:mortality* / Q000628:therapy; D006801:Humans; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012372:ROC Curve; D015996:Survival Rate
keywords: MRI; VASARI; glioblastoma; proportional hazards; survival analysis
references: Cancer Cell. 2010 Jan 19;17(1):98-110
references: 20129251
references: J Neurosurg. 2001 Aug;95(2):190-8
references: 11780887
references: BMC Bioinformatics. 2011 Mar 17;12:77
references: 21414208
references: PLoS One. 2012;7(8):e41522
references: 22870228
references: J Clin Oncol. 2007 Jun 20;25(18):2601-6
references: 17577040
references: Biometrics. 1988 Sep;44(3):837-45
references: 3203132
references: Stat Med. 2004 Jul 15;23(13):2109-23
references: 15211606
references: Clin Cancer Res. 2011 Nov 15;17(22):7148-55
references: 21914791
references: Radiology. 2013 May;267(2):560-9
references: 23392431
references: J Clin Oncol. 2010 Aug 20;28(24):3838-43
references: 20644085
references: Stat Med. 1996 Feb 28;15(4):361-87
references: 8668867
references: AJNR Am J Neuroradiol. 2005 Nov-Dec;26(10):2466-74
references: 16286386
references: Neuro Oncol. 2008 Feb;10(1):79-87
references: 17993634
references: Bioinformatics. 2008 Oct 1;24(19):2200-8
references: 18635567
references: Nature. 2008 Oct 23;455(7216):1061-8
references: 18772890
references: Neurosurgery. 2008 Mar;62(3):564-76; discussion 564-76
references: 18425006
references: Cancer. 2012 Sep 1;118(17):4201-11
references: 22294349