Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients.
GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-...
| Published in: | Medical & Biological Engineering & Computing Vol. 54; no. 11; pp. 1707 - 1719 |
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| Main Authors: | , |
| Format: | Journal Article |
| Published: |
Springer Nature
Nov2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118887918&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118887918 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2016 vid: 54 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 118887918 118887918 NLM26960324 10.1007/s11517-016-1461-5 NLM26960324 118887918 ppf: 1707 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients. aug: au: Chaddad, Ahmad Tanougast, Camel affil: Laboratory of Design, Optimization and Modeling (LCOMS) , University of Lorraine , 7 rue marconi Metz 57070 France sug: subj: Magnetic Resonance Imaging Methods Glioma Pathology Algorithms Aged, 80 and Over Aged Survival Analysis Adult Kaplan-Meier Estimator ROC Curve Phenotype Middle Age Aged, 80 & over Aged: 65+ years Adult: 19-44 years Middle Aged: 45-64 years ab: GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-based gray-level co-occurrence matrix (GLCM) approach and determine whether the texture features of phenotypes are related to patient survival. MR imaging data in 40 GBM patients were analyzed. Phenotypes vN, vAT, and vE were segmented in a preprocessing step using 3D Slicer for rigid registration by T1-weighted imaging and corresponding fluid attenuation inversion recovery images. The GBM phenotypes were segmented using 3D Slicer tools. Texture features were extracted from GLCM of GBM phenotypes. Thereafter, Kruskal-Wallis test was employed to select the significant features. Robust predictive GBM features were identified and underwent numerous classifier analyses to distinguish phenotypes. Kaplan-Meier analysis was also performed to determine the relationship, if any, between phenotype texture features and survival rate. The simulation results showed that the 22 texture features were significant with p value <0.05. GBM phenotype discrimination based on texture features showed the best accuracy, sensitivity, and specificity of 79.31, 91.67, and 98.75 %, respectively. Three texture features derived from active tumor parts: difference entropy, information measure of correlation, and inverse difference were statistically significant in the prediction of survival, with log-rank p values of 0.001, 0.001, and 0.008, respectively. Among 22 features examined, three texture features have the ability to predict overall survival for GBM patients demonstrating the utility of GLCM analyses in both the diagnosis and prognosis of this patient population. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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