A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme

IRBM

Volumn 36, Issue 6, 2015, Pages 345-350

  Upadhaya, T ^a,b,d   Morvan, Y ^a   Stindel, E ^b   Le Reste, P J ^c   Hatt, M ^b  

^a Bcom Institute of Research and Technologies   (France)

^b CNRS   (France)

^c PONTCHAILLOU UNIVERSITY HOSPITAL   (France)

^d UNIVERSITÉ DE BRETAGNE OCCIDENTALE   (France)

Author keywords

Image processing; Magnetic resonance imaging

Indexed keywords

ADULT; AGED; ARTICLE; CANCER PATIENT; CANCER PROGNOSIS; CLASSIFICATION ALGORITHM; CLINICAL ARTICLE; CONCEPTUAL FRAMEWORK; CONTRAST; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; GLIOBLASTOMA; HUMAN; IMAGE ANALYSIS; LEAVE ONE OUT CROSS VALIDATION; MACHINE LEARNING; MALE; MIDDLE AGED; MULTICENTER STUDY; MULTIMODAL IMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; OVERALL SURVIVAL; PREDICTIVE VALUE; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; THREE DIMENSIONAL IMAGING; TUMOR VOLUME; VALIDATION PROCESS; WORKFLOW;

EID: 84960498276     PISSN: 19590318     EISSN: 18760988     Source Type: Journal    
DOI: 10.1016/j.irbm.2015.08.001     Document Type: Article

References (24)

1. T.A. Dolecek, J.M. Propp, N.E. Stroup, and C. Kruchko CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009 Neuro-Oncol 14 suppl 5 2012 v1 v49
2. D.N. Louis, H. Ohgaki, O.D. Wiestler, W.K. Cavenee, P.C. Burger, A. Jouvet, and et al. The 2007 WHO classification of tumours of the central nervous system Acta Neuropathol 114 2 2007 97 109
3. D. Krex, B. Klink, C. Hartmann, A. von Deimling, T. Pietsch, M. Simon, and et al. Long-term survival with glioblastoma multiforme Brain 130 10 2007 2596 2606
4. M. Preusser, S. de Ribaupierre, A. Wohrer, S.C. Erridge, M. Hegi, M. Weller, and et al. Current concepts and management of glioblastoma Ann Neurol 70 1 2011 9 21
5. D.A. Gutman, L.A.D. Cooper, S.N. Hwang, C.A. Holder, J. Gao, T.D. Aurora, and et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set Radiology 267 2 2013 560 569
6. O. Gevaert, L.A. Mitchell, A.S. Achrol, J. Xu, S. Echegaray, G.K. Steinberg, and et al. Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features Radiology 2014
7. M.A. Mazurowski, J. Zhang, K.B. Peters, and H. Hobbs Computer-extracted MR imaging features are associated with survival in glioblastoma patients J Neuro-Oncol 2014 1 6
8. M. Lacroix, D. Abi-Said, D.R. Fourney, Z.L. Gokaslan, W. Shi, F. DeMonte, and et al. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival J Neurosurg 95 2 2001 190 198
9. L. Alic, W.J. Niessen, and J.F. Veenland Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review PLoS ONE 9 10 2014 e110300
10. F. Tixier, M. Hatt, C. Valla, V. Fleury, C. Lamour, S. Ezzouhri, and et al. Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer J Nucl Med 55 8 2014 1235 1241
11. F. Tixier, C. Cheze Le Rest, M. Hatt, N. Albarghach, O. Pradier, J.-P. Metges, and et al. Intratumor heterogeneity characterized by textural features on base-line 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer J Nucl Med 52 3 2011 369 378
12. E.I. Zacharaki, S. Wang, S. Chawla, D.S. Yoo, R. Wolf, E.R. Melhem, and et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme Magn Reson Med 62 6 2009 1609 1618
13. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach Learn 46 1-3 2002 389 422
14. L. Ibanez, W. Schroeder, L. Ng, and J. Cates The ITK software guide Second edition 2005 Kitware, Inc. 1-930934-15-7 http://www.itk.org/ItkSoftwareGuide.pdf
15. J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever Mutual-information-based registration of medical images: a survey IEEE Trans Med Imaging 22 8 2003 986 1004
16. N. Porz, S. Bauer, A. Pica, P. Schucht, J. Beck, R. Kumar Verma, and et al. Multi-modal glioblastoma segmentation: man versus machine PLoS ONE 9 5 2014 e96873
17. S. Bauer, L.-P. Nolte, and M. Reyes Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization Medical image computing and computer-assisted intervention - MICCAI 2011 2011 Springer 354 361
18. R.M. Haralick, K. Shanmugam, and I.H. Dinstein Textural features for image classification IEEE Trans Syst Man Cybern 6 1973 610 621
19. H.-H. Loh, J.-G. Leu, and R.C. Luo The analysis of natural textures using run length features IEEE Trans Ind Electron 35 2 1988 323 328
20. G. Thibault, B. Fertil, C. Navarro, and et al. Texture indexes and gray level size zone matrix: application to cell nuclei classification Pattern Recognit Inf Process 2009 140 145
21. Gutierrez, D. Rodriguez, and et al. Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors Am J Neuroradiol 35 5 2014 1009 1015
22. G. Xian An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM Expert Syst Appl 37 10 2010 6737 6741
23. M. Vallieres, and et al. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities Phys Med Biol 60 14 2015 5471
24. L. Evers, and C.-M. Messow Sparse kernel methods for high-dimensional survival data Bioinformatics 24 14 2008 1632 1638