Dergi makalesi Açık Erişim
Citak-Er, Fusun; Vural, Metin; Acar, Omer; Esen, Tarik; Onay, Aslihan; Ozturk-Isik, Esin
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Vural, Metin</subfield> <subfield code="u">VKF Amer Hosp, Dept Radiol, TR-34365 Istanbul, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Acar, Omer</subfield> <subfield code="u">VKF Amer Hosp, Dept Urol, TR-34365 Istanbul, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Esen, Tarik</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Onay, Aslihan</subfield> <subfield code="u">VKF Amer Hosp, Dept Radiol, TR-34365 Istanbul, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Ozturk-Isik, Esin</subfield> <subfield code="u">Bogazici Univ, Inst Biomed Engn, TR-34684 Istanbul, Turkey</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="p">BIOMED RESEARCH INTERNATIONAL</subfield> <subfield code="v">2014</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="a">Creative Commons Attribution</subfield> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1155/2014/690787</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Citak-Er, Fusun</subfield> <subfield code="u">Yeditepe Univ, Dept Genet & Bioengn, TR-34755 Istanbul, Turkey</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:zenodo.org:64255</subfield> <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2014-01-01</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/64255/files/bib-7588fc24-f10a-44dc-bead-418e0b9be3b6.txt</subfield> <subfield code="z">md5:e43d98b0800d1f5d9cb078d0c2671a55</subfield> <subfield code="s">283</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <controlfield tag="005">20210316015126.0</controlfield> <controlfield tag="001">64255</controlfield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a">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.</subfield> </datafield> </record>
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