Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.

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.

Mental health training in emergency homeless shelters.

The prevalence of mental illness among homeless persons points to the importance of providing mental health training to emergency shelter staff. The authors report on their own work and argue that such training offers the potential to significantly improve shelter staffs ability to respond to the needs of shelter residents with mental illness, and to the behavioral problems some of these individuals may pose for shelter operation. Mental health care providers should take into consideration organizational dynamics when planning and implementing such training.