Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 8Citation - Scopus: 10Lung Cancer Subtype Differentiation From Positron Emission Tomography Images(Tubitak Scientific & Technological Research Council Turkey, 2020-01-27) Ayyildiz, Oguzhan; Aydin, Zafer; Yilmaz, Bulent; Karacavus, Seyhan; Senkaya, Kubra; Icer, Semra; Kaya, Eser; Taşdemir, ArzuLung cancer is one of the deadly cancer types, and almost 85% of lung cancers are nonsmall cell lung cancer (NSCLC). In the present study we investigated classification and feature selection methods for the differentiation of two subtypes of NSCLC, namely adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). The major advances in understanding the effects of therapy agents suggest that future targeted therapies will be increasingly subtype specific. We obtained positron emission tomography (PET) images of 93 patients with NSCLC, 39 of which had ADC while the rest had SqCC. Random walk segmentation was applied to delineate three-dimensional tumor volume, and 39 texture features were extracted to grade the tumor subtypes. We examined 11 classifiers with two different feature selection methods and the effect of normalization on accuracy. The classifiers we used were the k-nearest-neighbor, logistic regression, support vector machine, Bayesian network, decision tree, radial basis function network, random forest, AdaBoostM1, and three stacking methods. To evaluate the prediction accuracy we performed a leave-one-out cross-validation experiment on the dataset. We also considered optimizing certain hyperparameters of these models by performing 10-fold cross-validation separately on each training set. We found that the stacking ensemble classifier, which combines a decision tree, AdaBoostM1, and logistic regression methods by a metalearner, was the most accurate method for detecting subtypes of NSCLC, and normalization of feature sets improved the accuracy of the classification method.Article Citation - WoS: 79Citation - Scopus: 96Effect of Granulated Blast Furnace Slag and Fly Ash Addition on the Strength Properties of Lightweight Mortars Containing Waste PET Aggregates(Elsevier Sci Ltd, 2011-10) Akcaozoglu, Semiha; Atis, Cengiz Duran; Akaözolu, Semiha; Ati, Cengiz DuranIn this work, the effect of Granulated Blast Furnace Slag (GBFS) and fly ash (FA) addition on the strength properties of lightweight mortars containing waste Poly-ethylene Terephthalate (PET) bottle aggregates was investigated. Investigation was carried out on three groups of mortar specimens. One made with only Normal Portland cement (NPC) as binder, second made with NPC and GBFS together and, third made with NPC and FA together. The industrial wastes mentioned above were used as the replacement of cement on mass basis at the replacement ratio of 50%. The size of shredded PET granules used as aggregate for the preparation of mortar mixtures were between 0 and 4 mm. The waste lightweight PET aggregate (WPLA)-binder ratio (WPLA/b) was 0.60; the water-binder (w/b) ratios were determined as 0.45 and 0.50. The dry unit weight, compressive and flexural-tensile strengths, carbonation depths and drying shrinkage values were measured and presented. The results have shown that modifying GBFS had positive effects on the compressive strength and drying shrinkage values (after 90 days) of the WPLA mortars. However, FA substitution decreased compressive and flexural-tensile strengths and increased carbonation depths. Nevertheless a visible reduction occurred on the drying shrinkage values of FA modifying specimens more than cement specimens and GBFS modified specimens. The test results indicated that, GBFS has a potential of using as the replacement of cement on the WPLA mortars by taking into consideration the characteristics. But using FA as a binder at the replacement ratio of 50% did not improve the overall strength properties. Although it was thought that, using FA as binder at the replacement ratio of 50% for the aim of production WPLA concrete which has a specific strength, would provide advantages of economical and ecological aspects. (C) 2011 Elsevier Ltd. All rights reserved.Article Citation - WoS: 17Citation - Scopus: 18Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC(Springer, 2017-07-06) Karacavus, Seyhan; Yilmaz, Bulent; Tasdemir, Arzu; Kayaalti, Omer; Kaya, Eser; Icer, Semra; Ayyildiz, OguzhanWe 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.