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Imaging biomarker analysis of advanced multiparametric MRI for glioma grading
dc.creator | Vamvakas A., Williams S.C., Theodorou K., Kapsalaki E., Fountas K., Kappas C., Vassiou K., Tsougos I. | en |
dc.date.accessioned | 2023-01-31T10:25:32Z | |
dc.date.available | 2023-01-31T10:25:32Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1016/j.ejmp.2019.03.014 | |
dc.identifier.issn | 11201797 | |
dc.identifier.uri | http://hdl.handle.net/11615/80374 | |
dc.description.abstract | Aims and objectives: To investigate the value of advanced multiparametric MR imaging biomarker analysis based on radiomic features and machine learning classification, in the non-invasive evaluation of tumor heterogeneity towards the differentiation of Low Grade vs. High Grade Gliomas. Methods and materials: Forty histologically confirmed glioma patients (20 LGG and 20 HGG) who underwent a standard 3T-MRI tumor protocol with conventional (T1 pre/post-contrast, T2-FSE, T2-FLAIR) and advanced techniques (Diffusion Tensor and Perfusion Imaging, 1H-MR Spectroscopy), were included. A semi-automated segmentation technique, based on T1W-C and DTI, was used for tumor core delineation in all available parametric maps. 3D Texture analysis considered 12 Histogram, 11 Co-Occurrence Matrix (GLCM) and 5 Run Length Matrix (GLRLM) features, derived from p, q, MD, FA, T1W-C, T2W-FSE, T2W-FLAIR and raw DSCE data. Along with 1H-MRS metabolic ratios and mean rCBV values, a total of 581 attributes for each subject were obtained. A Support Vector Machine – Recursive Feature Elimination (SVM-RFE) algorithm and SVM classifier were utilized for feature selection and classification, respectively. Results: Three different SVM classifiers were evaluated with consecutively SVM-RFE feature subsets. Linear SMO classifier demonstrated the highest performance for determining the optimal feature subset. Finally, 21 SVM-RFE top-ranked features were adopted, for training and testing the SMO classifier with leave-one-out cross-validation, achieving 95.5% Accuracy, 95% Sensitivity, 96% Specificity and 95.5% Area Under ROC Curve. Conclusion: Results demonstrate that quantitative analysis of phenotypic characteristics, based on advanced multiparametric MR neuroimaging data and texture features, utilizing state-of-the-art radiomic analysis methods, can significantly contribute to the pre-treatment glioma grade differentiation. © 2019 Associazione Italiana di Fisica Medica | en |
dc.language.iso | en | en |
dc.source | Physica Medica | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063205125&doi=10.1016%2fj.ejmp.2019.03.014&partnerID=40&md5=7837d6c1d5c47b902c8043012fab5b95 | |
dc.subject | biological marker | en |
dc.subject | tumor marker | en |
dc.subject | area under the curve | en |
dc.subject | Article | en |
dc.subject | cancer grading | en |
dc.subject | classification algorithm | en |
dc.subject | clinical article | en |
dc.subject | clinical feature | en |
dc.subject | diagnostic accuracy | en |
dc.subject | diffusion tensor imaging | en |
dc.subject | glioma | en |
dc.subject | histology | en |
dc.subject | human | en |
dc.subject | human tissue | en |
dc.subject | image quality | en |
dc.subject | image segmentation | en |
dc.subject | multiparametric magnetic resonance imaging | en |
dc.subject | neuroimaging | en |
dc.subject | perfusion weighted imaging | en |
dc.subject | phenotype | en |
dc.subject | proton nuclear magnetic resonance | en |
dc.subject | quantitative analysis | en |
dc.subject | receiver operating characteristic | en |
dc.subject | sensitivity and specificity | en |
dc.subject | support vector machine | en |
dc.subject | brain | en |
dc.subject | brain tumor | en |
dc.subject | cancer grading | en |
dc.subject | computer assisted diagnosis | en |
dc.subject | diagnostic imaging | en |
dc.subject | glioma | en |
dc.subject | nuclear magnetic resonance imaging | en |
dc.subject | pathology | en |
dc.subject | procedures | en |
dc.subject | three dimensional imaging | en |
dc.subject | Biomarkers, Tumor | en |
dc.subject | Brain | en |
dc.subject | Brain Neoplasms | en |
dc.subject | Glioma | en |
dc.subject | Humans | en |
dc.subject | Image Interpretation, Computer-Assisted | en |
dc.subject | Imaging, Three-Dimensional | en |
dc.subject | Magnetic Resonance Imaging | en |
dc.subject | Neoplasm Grading | en |
dc.subject | Sensitivity and Specificity | en |
dc.subject | Support Vector Machine | en |
dc.subject | Associazione Italiana di Fisica Medica | en |
dc.title | Imaging biomarker analysis of advanced multiparametric MRI for glioma grading | en |
dc.type | journalArticle | en |
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