Name
Title | ||
|---|---|---|
Multivariate discriminant analysis of multiparametric brain MRI to differentiate high grade and low grade gliomas — A computer-aided diagnosis development study |
Abstract | ||
|---|---|---|
The aim of this study is to investigate the predictive capacity of multiparametric magnetic resonance imaging (MRI) findings using multivariate discriminant analysis. Preoperative clinical findings and multiparametric MRI, including diffusion weighted MR imaging, diffusion tensor imaging, perfusion MR imaging and MR spectroscopic imaging, were used as predictors to distinguish high grade from low grade gliomas. Principal component analysis was performed prior to discriminant analysis for dimensional reduction. Linear and quadratic discriminant analysis were performed and compared based on sensitivity and specificity analysis. The sensitivities of linear and quadratic discriminant analysis were 76.5% and 83.5%, respectively. Their specificities were 68.5% and 46.5%, respectively. Quadratic discriminant analysis provided a better discrimination than linear discriminant analysis for this dataset. This study is a model for a computer aided diagnosis system for glioma grading. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBE.2013.6701649 | Bioinformatics and Bioengineering |
Keywords | Field | DocType |
biodiffusion,biomedical MRI,brain,medical image processing,principal component analysis,sensitivity analysis,tumours,MR spectroscopic imaging,computer aided diagnosis system,computer-aided diagnosis development study,diffusion tensor imaging,diffusion weighted MR imaging,dimensional reduction,glioma grading,high grade gliomas,linear discriminant analysis,low grade gliomas,magnetic resonance imaging,multiparametric brain MRI,multivariate discriminant analysis,perfusion MR imaging,primary tumor,principal component analysis,quadratic discriminant analysis,sensitivity analysis,specificity analysis | Nuclear medicine,Diffusion MRI,Multiparametric Magnetic Resonance Imaging,Multivariate statistics,Computer science,Glioma,Computer-aided diagnosis,Artificial intelligence,Linear discriminant analysis,Principal component analysis,Machine learning,Quadratic classifier | Conference |
ISSN | Citations | PageRank |
2471-7819 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Er, F.C. | 1 | 0 | 0.34 |
| Zeynep Firat | 2 | 9 | 2.52 |
| Kovanlikaya, I. | 3 | 0 | 0.34 |
| Ture, U. | 4 | 3 | 1.15 |