Browsing by Subject "Cancer diagnosis"

Now showing 1 - 7 of 7

Open Access
Biosystems engineering of prokaryotes with tumor-killing capacities
(Bentham Science Publishers Ltd., 2016) Kalyoncu, E.; Olmez, T. T.; Ozkan, A. D.; Sarioglu, O. F.
Certain bacteria selectively attack tumor tissues and trigger tumor shrinkage by producing toxins and modulating the local immune system, but their clinical utility is limited because of the dangers posed by systemic infection. Genetic engineering can be used to minimize the risks associated with tumor-targeting pathogens, as well as to increase their efficiency in killing tumor cells. Advances in genetic circuit design have led to the development of bacterial strains with enhanced tumor-targeting capacities and the ability to secrete therapeutics, cytotoxic proteins and prodrug-cleaving enzymes, which allows their safe and effective use for cancer treatment. The present review details the recent advances in the design and application of these modified bacterial strains.
Open Access
Cell-graph mining for breast tissue modeling and classification
(IEEE, 2007-08) Bilgin, C.; Demir, Çiğdem; Nagi, C.; Yener, B.
We consider the problem of automated cancer diagnosis in the context of breast tissues. We present graph theoretical techniques that identify and compute quantitative metrics for tissue characterization and classification. We segment digital images of histopatological tissue samples using k-means algorithm. For each segmented image we generate different cell-graphs using positional coordinates of cells and surrounding matrix components. These cell-graphs have 500-2000 cells(nodes) with 1000-10000 links depending on the tissue and the type of cell-graph being used. We calculate a set of global metrics from cell-graphs and use them as the feature set for learning. We compare our technique, hierarchical cell graphs, with other techniques based on intensity values of images, Delaunay triangulation of the cells, the previous technique we proposed for brain tissue images and with the hybrid approach that we introduce in this paper. Among the compared techniques, hierarchical-graph approach gives 81.8% accuracy whereas we obtain 61.0%, 54.1% and 75.9% accuracy with intensity-based features, Delaunay triangulation and our previous technique, respectively. © 2007 IEEE.
Open Access
Color graphs for automated cancer diagnosis and grading
(Institute of Electrical and Electronics Engineers, 2010-03) Altunbay, D.; Cigir, C.; Sokmensuer, C.; Gunduz Demir, C.
This paper reports a new structural method to mathematically represent and quantify a tissue for the purpose of automated and objective cancer diagnosis and grading. Unlike the previous structural methods, which quantify a tissue considering the spatial distributions of its cell nuclei, the proposed method relies on the use of distributions of multiple tissue components for the representation. To this end, it constructs a graph on multiple tissue components and colors its edges depending on the component types of their endpoints. Subsequently, it extracts a new set of structural features from these color graphs and uses these features in the classification of tissues. Working with the images of colon tissues, our experiments demonstrate that the color-graph approach leads to 82.65% test accuracy and that it significantly improves the performance of its counterparts. © 2006 IEEE.
Open Access
Kanser tanısı için kolon bezlerinin matematiksel analizi
(IEEE, 2009-04) Çığır, Celal; Sökmensüer, C.; Gündüz-Demir, Çiğdem
Neoplastic diseases including cancer cause organizational changes in tissues. Histopathological examination, which is routinely used for the diagnosis and grading of these diseases, relies on pathologists to identify such tissue changes under a microscope. However, as this examination mainly relies on the visual interpretation of pathologists, it may lead to a considerable amount of subjectivity. To reduce the subjectivity level, it is proposed to use computational methods that provide objective measures. These methods quantify the tissue changes associated with disease by defining features on tissue images. In this paper, colon glands are mathematically analyzed making use of different feature extraction approaches. In this analysis, morphological, intensity-based, and textural features are investigated and glands are classified using these features. Working on the images of 108 colon tissues of 36 patients, our experiments demonstrate that this classification leads to promising results for differentiating normal glands from the cancerous ones. ©2009 IEEE.
Open Access
Kelime histogram modeli ile histopatolojik görüntü sınıflandırılması
(IEEE, 2011-04) Özdemir, Erdem; Sökmensüer, C.; Gündüz-Demir, Çiğdem
Colon cancer, which is one of the most common cancer type, could be cured with its early diagnosis. In the current practice of medicine, there are many screening techniques such as colonoscopy, sigmoidoscopy, and stool test, however the most effective and most widely used method for cancer diagnosis is to take tissue sections with biopsy and examine them under a microscope. As this examination is based on visual interpretation, it may lead to subjective decisions and diagnostic differences among pathologists. The need of reducing inter-variability in cancer diagnosis has led to studies for extraction of features from biopsy images and development of algorithms that give objective results. In this paper, we propose a method for the automated classification of a colon tissue image with the features extracted from a histogram that models the existence of image regions determined in an unsupervised way. Experiments on colon tissue images show that the proposed method leads to more successful results compared to its counterparts. Moreover, the proposed method, which uses color intensities for feature extraction, has the potential of giving better results with the use of additional features. © 2011 IEEE.
Open Access
Lack of association between the MDM2-SNP309 polymorphism and breast cancer risk
(Delinasios GJ & CO, 2006) Petenkaya, A.; Bozkurt, B.; Akilli-Ozturk, O.; Kaya, H. S.; Gur-Dedeoglu, B.; Yulug, I. G.
Background: A T-to-G polymorphism (SNP309) at the promoter region of MDM2 has been recently reported to extend the Sp1 binding site that positively regulates the MDM2 transcription level and consequently, its expression level. MDM2 is the negative regulator of p53 tumor suppressor protein and elevated levels of MDM2 hamper the stress response driven by the p53 pathway. Whether MDM2-SNP309 was associated with breast cancer as a predisposing factor was investigated. Patients and Methods: A case-control study of 223 females diagnosed with breast cancer and 149 female controls sampled from the Turkish population was carried out and the T/G MDM2-SNP309 genotype of participants was determined. Results: There was no significant association of the G/G or G/T genotypes with breast cancer risk (odds ratio (OR) 1.14, 95% confidence interval (CI) 0.59-2.22, and OR 1.20, 95% CI 0.67-2.12, respectively). Stratification of the data for onset age or for menopausal status at the time of diagnosis also revealed no association for either group.
Open Access
PPAR-alpha L162V polymorphism in human hepatocellular carcinoma
(Turkish Society of Gastroenterology, 2008) Koytak, E. S.; Mızrak, D.; Bektaş, M.; Verdi, H.; Arslan-Ergül, Ayça; İdilman, R.; Çınar, K.; Yurdaydın, C.; Ersöz, S.; Karayalçın, K.; Uzunalimoğlu, Ö.; Bozkaya, H.
Background/aims: Several lines of evidence suggest that peroxisome proliferator-activated receptor alpha may be involved in hepatocarcinogenesis. L162V polymorphism of the peroxisome proliferator-activated receptor alpha gene enhances the transactivation activity of this transcription factor. The aim of this study was to determine the frequency and clinical correlates of peroxisome proliferator-activated receptor alpha L162V polymorphism in hepatitis virus-induced hepatocellular carcinoma. Methods: 90 hepatocellular carcinoma patients diagnosed at Ankara University Gastroenterology Clinic between January 2002 and July 2003 and 80 healthy controls with normal body mass index, blood chemistry and with negative viral serology were included. peroxisome proliferator-activated receptor alpha L162V polymorphism was determined by PCR-RFLP. Results: hepatocellular carcinoma etiologies were as follows: 56 HBV, 12 HBV+HDV, 22 HCV. Eighty-seven patients (97%) were cirrhotic, and 60 patients (67.5%) had advanced tumors. In 83 (92%) of 90 hepatocellular carcinoma patients, gene segment including polymorphic region could be amplified by PCR (50 HBV, 12 HBV+HDV, 21 HCV) and 6 of them (7.2%, all infected with HBV) had L162V polymorphism, while 2 (2.5%) of 80 controls had this polymorphism (p=0.162). This trend became more remarkable when only HBV (HBV+HDV)-infected patients were compared with controls (6/62, 9.7% vs. 2/80, 2.5%, respectively, p=0.071). Five of 6 patients with L162V had advanced disease. Conclusions: Peroxisome proliferator-activated receptor alpha L162V polymorphism tends to occur in HBV-induced epatocellular carcinoma and is absent in HCV-related epatocellular carcinoma. These findings may show clues for the existence of different carcinogenesis mechanisms in these two common etiologies. Frequent occurrence of advanced disease in patients with L162V polymorphism suggests a role for this polymorphism in tumor progression.