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dc.contributor.authorKaracavus, Seyhan
dc.contributor.authorYılmaz, Bülent
dc.contributor.authorTasdemir, Arzu
dc.contributor.authorKayaaltı, Ömer
dc.contributor.authorKaya, Eser
dc.contributor.authorİçer, Semra
dc.contributor.authorAyyıldız, Oguzhan
dc.identifier.citationJOURNAL OF DIGITAL IMAGING Volume: 31 Issue: 2 Pages: 210-223 DOI: 10.1007/s10278-017-9992-3en_US
dc.identifier.issneISSN: 1618-727X
dc.identifier.otherPubMed ID: 28685320
dc.identifier.otherAccession Number: WOS:000428438400010
dc.identifier.otherDOI: 10.1007/s10278-017-9992-3
dc.descriptionThis study was funded by TUBITAK (The Scientific and Technological Research Council of Turkey) under Project No.: 113E188.en_US
dc.description.abstractWe investigated the association between the textural features obtained from F-18-FDG images, metabolic parameters (SUVmax(,) SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technological Research Council of Turkey) - 113E188en_US
dc.publisherSPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USAen_US
dc.relation.ispartofseriesJOURNAL OF DIGITAL IMAGING;Volume: 31 Issue: 2 Pages: 210-223
dc.subjectTexture analysisen_US
dc.subjectTumor heterogeneityen_US
dc.subjectTumor histopathological characteristicsen_US
dc.titleCan Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?en_US
dc.contributor.departmentAGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümüen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US

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