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BioMed Research International
Volume 2014, Article ID 690787, 9 pages
Research Article

Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T

1Department of Genetics and Bioengineering, Yeditepe University, İnönü Mah., Kayışdağı Cad, 26 Ağustos Yerleşimi, Ataşehir, 34755 Istanbul, Turkey
2Department of Radiology, VKF American Hospital, 34365 Istanbul, Turkey
3Department of Urology, VKF American Hospital, 34365 Istanbul, Turkey
4School of Medicine, Koç University, 34450 Istanbul, Turkey
5Biomedical Engineering Institute, Boğaziçi University, Rasathane Cad, Kandilli Campus, Kandilli Mah., 34684 Istanbul, Turkey

Received 5 June 2014; Revised 10 September 2014; Accepted 12 September 2014; Published 2 December 2014

Academic Editor: Barıs Turkbey

Copyright © 2014 Fusun Citak-Er et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.