Browsing by Author "İçer, Semra"

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    Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?
    (SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA, 2018) Karacavus, Seyhan; Yılmaz, Bülent; Tasdemir, Arzu; Kayaaltı, Ömer; Kaya, Eser; İçer, Semra; Ayyıldız, Oguzhan; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü;
    We 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.
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    Comparison of 3D texture features and metabolic parameters obtained from 18-F FDG-PET/CT images for evaluating tumor stage and subtype in non-small cell lung cancer
    (SPRINGER, 2015) Karacavus, S.; Yılmaz, Bülent; Ayyildiz, Oğuzhan; Kayaalti, O.; Tasdemir, A.; Kaya, E.; İçer, Semra; Eset, K.; Vardareli, E.; Asyali, M. H.; 0000-0002-8473-9720; 0000-0003-2954-1217; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Yılmaz, Bülent; Ayyildiz, Oğuzhan
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