Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation

Abstract. We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point (threshold=0.5) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point (threshold=0.166) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.

[1]  M. Fox,et al.  Fractal feature analysis and classification in medical imaging. , 1989, IEEE transactions on medical imaging.

[2]  B. O'neill,et al.  Glioblastoma survival in the United States before and during the temozolomide era , 2012, Journal of Neuro-Oncology.

[3]  Jordan M. Malof,et al.  Imaging descriptors improve the predictive power of survival models for glioblastoma patients. , 2013, Neuro-oncology.

[4]  Anant Madabhushi,et al.  Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification , 2011, Journal of Digital Imaging.

[5]  R. Gillies,et al.  Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. , 2014, Translational oncology.

[6]  E Le Rumeur,et al.  MRI texture analysis on texture test objects, normal brain and intracranial tumors. , 2003, Magnetic resonance imaging.

[7]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[8]  Vicky Goh,et al.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[9]  Chun-Ta Liao,et al.  Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma , 2013, The Journal of Nuclear Medicine.

[10]  M. Hatt,et al.  Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.

[11]  A. Børresen-Dale,et al.  The landscape of cancer genes and mutational processes in breast cancer , 2012, Nature.

[12]  Mark J. Ratain,et al.  Tumour heterogeneity in the clinic , 2013, Nature.

[13]  V. Goh,et al.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.

[14]  Ron Kikinis,et al.  Markov random field segmentation of brain MR images , 1997, IEEE Transactions on Medical Imaging.

[15]  Hod Lipson,et al.  Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.

[16]  K. Polyak,et al.  Tumor heterogeneity: causes and consequences. , 2010, Biochimica et biophysica acta.

[17]  Alejandro Martínez-Abraín,et al.  Statistical significance and biological relevance: A call for a more cautious interpretation of results in ecology , 2008 .

[18]  Sally Freels,et al.  Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. , 2010, Neuro-oncology.

[19]  S. Niclou,et al.  EGFR wild-type amplification and activation promote invasion and development of glioblastoma independent of angiogenesis , 2013, Acta Neuropathologica.

[20]  Yoshitaka Narita,et al.  Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. , 2010, Genes & development.

[21]  A. Magurran,et al.  Measuring Biological Diversity , 2004 .

[22]  A. Baselga Partitioning the turnover and nestedness components of beta diversity , 2010 .

[23]  N. Graham,et al.  Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation , 2002 .

[24]  C. McArdle,et al.  Prospective study of colorectal cancer in the West of Scotland: 10‐year follow‐up , 1990, The British journal of surgery.

[25]  Raul Rabadan,et al.  The integrated landscape of driver genomic alterations in glioblastoma , 2013, Nature Genetics.

[26]  R. Whittaker Evolution and measurement of species diversity , 1972 .

[27]  M. Giger,et al.  Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. , 2006, Medical physics.

[28]  Balaji Ganeshan,et al.  Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. , 2009, Radiology.

[29]  W. Pope Genomics of brain tumor imaging. , 2015, Neuroimaging clinics of North America.

[30]  Douglas A. Reynolds Gaussian Mixture Models , 2009, Encyclopedia of Biometrics.

[31]  H. Ellis,et al.  Current Therapeutic Advances Targeting EGFR and EGFRvIII in Glioblastoma , 2015, Front. Oncol..

[32]  R. Gillies,et al.  Quantitative imaging in cancer evolution and ecology. , 2013, Radiology.

[33]  L. Chin,et al.  Mig-6 controls EGFR trafficking and suppresses gliomagenesis , 2010, Proceedings of the National Academy of Sciences.

[34]  R. Whittaker Vegetation of the Siskiyou Mountains, Oregon and California , 1960 .

[35]  Kyle Summers,et al.  Evolutionary biology of cancer. , 2005, Trends in ecology & evolution.

[36]  Akinobu Shimizu,et al.  Medical image analysis of 3D CT images based on extension of Haralick texture features , 2008, Comput. Medical Imaging Graph..