![](/images/graphics-bg.png)
Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T
Joint Authors
Citak-Er, Fusun
Vural, Metin
Onay, Aslihan
Ozturk-Isik, Esin
Acar, Omer
Esen, Tarık
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-02
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Objective.
This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters.
Materials and Methods.
Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study.
The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist.
SVM based recursive feature elimination (SVM-RFE) was used for eliminating features.
Principal component analysis (PCA) was applied for data uncorrelation.
Results.
Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively.
Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.
Conclusion.
SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.
American Psychological Association (APA)
Citak-Er, Fusun& Vural, Metin& Acar, Omer& Esen, Tarık& Onay, Aslihan& Ozturk-Isik, Esin. 2014. Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T. BioMed Research International،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1034536
Modern Language Association (MLA)
Citak-Er, Fusun…[et al.]. Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T. BioMed Research International No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1034536
American Medical Association (AMA)
Citak-Er, Fusun& Vural, Metin& Acar, Omer& Esen, Tarık& Onay, Aslihan& Ozturk-Isik, Esin. Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1034536
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034536