Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme

One of the most common and aggressive malignant brain tumors is Glioblastoma multiforme. Despite the multimodality treatment such as radiation therapy and chemotherapy (temozolomide: TMZ), the median survival rate of glioblastoma patient is less than 15 months. In this study, we investigated the association between measures of spatial diversity derived from spatial point pattern analysis of multiparametric magnetic resonance imaging (MRI) data with molecular status as well as 12-month survival in glioblastoma. We obtained 27 measures of spatial proximity (diversity) via spatial point pattern analysis of multiparametric T1 post-contrast and T2 fluid-attenuated inversion recovery MRI data. These measures were used to predict 12-month survival status (≤12 or >12 months) in 74 glioblastoma patients. Kaplan-Meier with receiver operating characteristic analyses was used to assess the relationship between derived spatial features and 12-month survival status as well as molecular subtype status in patients with glioblastoma. Kaplan-Meier survival analysis revealed that 14 spatial features were capable of stratifying overall survival in a statistically significant manner. For prediction of 12-month survival status based on these diversity indices, sensitivity and specificity were 0.86 and 0.64, respectively. The area under the receiver operating characteristic curve and the accuracy were 0.76 and 0.75, respectively. For prediction of molecular subtype status, proneural subtype shows highest accuracy of 0.93 among all molecular subtypes based on receiver operating characteristic analysis. We find that measures of spatial diversity from point pattern analysis of intensity habitats from T1 post-contrast and T2 fluid-attenuated inversion recovery images are associated with both tumor subtype status and 12-month survival status and may therefore be useful indicators of patient prognosis, in addition to providing potential guidance for molecularly-targeted therapies in Glioblastoma multiforme.

[1]  Robert J. Gillies,et al.  Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma , 2015, PloS one.

[2]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

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

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

[5]  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.

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

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

[8]  Seung‐Mo Hong,et al.  K-Adaptive Partitioning for Survival Data with an Application to SEER: The kaps Add-on Package for R , 2013 .

[9]  Seung-Mo Hong,et al.  K-Adaptive Partitioning for Survival Data: The kaps Add-on Package for R , 2013 .

[10]  J. Illian,et al.  A family of spatial biodiversity measures based on graphs , 2012, Environmental and Ecological Statistics.

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

[12]  S. Torquato,et al.  Spatial Organization and Correlations of Cell Nuclei in Brain Tumors , 2011, PloS one.

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

[14]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[15]  D. Bigner,et al.  Overall survival of newly diagnosed glioblastoma patients receiving carmustine wafers followed by radiation and concurrent temozolomide plus rotational multiagent chemotherapy , 2009, Cancer.

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

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

[18]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[19]  D. Stoyan,et al.  Statistical Analysis and Modelling of Spatial Point Patterns , 2008 .

[20]  Wolfgang Weil,et al.  Spatial Point Processes and their Applications , 2007 .

[21]  Adrian Baddeley,et al.  Spatial Point Processes and their Applications , 2007 .

[22]  Adrian Baddeley,et al.  spatstat: An R Package for Analyzing Spatial Point Patterns , 2005 .

[23]  R. Solé,et al.  Metapopulation dynamics and spatial heterogeneity in cancer , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Matthew J. McAuliffe,et al.  Medical Image Processing, Analysis and Visualization in clinical research , 2001, Proceedings 14th IEEE Symposium on Computer-Based Medical Systems. CBMS 2001.

[26]  van Marie-Colette Lieshout,et al.  Indices of Dependence Between Types in Multivariate Point Patterns , 1999 .

[27]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

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

[29]  P. Diggle,et al.  Spatial point pattern analysis and its application in geographical epidemiology , 1996 .

[30]  Melinda Moeur,et al.  Characterizing Spatial Patterns of Trees Using Stem-Mapped Data , 1993, Forest Science.

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