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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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/64255</identifier>
  <creators>
    <creator>
      <creatorName>Citak-Er, Fusun</creatorName>
      <givenName>Fusun</givenName>
      <familyName>Citak-Er</familyName>
      <affiliation>Yeditepe Univ, Dept Genet &amp; Bioengn, TR-34755 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Vural, Metin</creatorName>
      <givenName>Metin</givenName>
      <familyName>Vural</familyName>
      <affiliation>VKF Amer Hosp, Dept Radiol, TR-34365 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Acar, Omer</creatorName>
      <givenName>Omer</givenName>
      <familyName>Acar</familyName>
      <affiliation>VKF Amer Hosp, Dept Urol, TR-34365 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Esen, Tarik</creatorName>
      <givenName>Tarik</givenName>
      <familyName>Esen</familyName>
    </creator>
    <creator>
      <creatorName>Onay, Aslihan</creatorName>
      <givenName>Aslihan</givenName>
      <familyName>Onay</familyName>
      <affiliation>VKF Amer Hosp, Dept Radiol, TR-34365 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ozturk-Isik, Esin</creatorName>
      <givenName>Esin</givenName>
      <familyName>Ozturk-Isik</familyName>
      <affiliation>Bogazici Univ, Inst Biomed Engn, TR-34684 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Final Gleason Score Prediction Using Discriminant Analysis And Support Vector Machine Based On Preoperative Multiparametric Mr Imaging Of Prostate Cancer At 3T</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2014</publicationYear>
  <dates>
    <date dateType="Issued">2014-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/64255</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1155/2014/690787</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="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.</description>
  </descriptions>
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