1 Ocak 2014 Dergi makalesi Açık Erişim

Citak-Er, Fusun; Vural, Metin; Acar, Omer; Esen, Tarik; Onay, Aslihan; Ozturk-Isik, Esin

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{
  "DOI": "10.1155/2014/690787", 
  "abstract": "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% andmean 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.", 
  "author": [
    {
      "family": "Citak-Er", 
      "given": " Fusun"
    }, 
    {
      "family": "Vural", 
      "given": " Metin"
    }, 
    {
      "family": "Acar", 
      "given": " Omer"
    }, 
    {
      "family": "Esen", 
      "given": " Tarik"
    }, 
    {
      "family": "Onay", 
      "given": " Aslihan"
    }, 
    {
      "family": "Ozturk-Isik", 
      "given": " Esin"
    }
  ], 
  "container_title": "BIOMED RESEARCH INTERNATIONAL", 
  "id": "64255", 
  "issued": {
    "date-parts": [
      [
        2014, 
        1, 
        1
      ]
    ]
  }, 
  "title": "Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T", 
  "type": "article-journal", 
  "volume": "2014"
}