Journal
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
Volume 60, Issue -, Pages 188-198Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2019.03.014
Keywords
Glioma grading; Multiparametric MRI; Radiomics; Machine learning
Funding
- Wellcome/EPSRC Centre for supporting Medical Engineering [WT 203148/Z/16/Z]
- NIHR
- UK Research and Innovation (UKRI)
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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.
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