Browsing by Subject "Biopsy"

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Open Access
Design of a novel MRI compatible manipulator for image guided prostate interventions
(IEEE, 2005-02) Krieger, A.; Susil, R. C.; Ménard, C.; Coleman, J. A.; Fichtinger, G.; Atalar, Ergin; Whitcomb, L. L.
This paper reports a novel remotely actuated manipulator for access to prostate tissue under magnetic resonance imaging guidance (APT-MRI) device, designed for use in a standard high-field MRI scanner. The device provides three-dimensional MRI guided needle placement with millimeter accuracy under physician control. Procedures enabled by this device include MRI guided needle biopsy, fiducial marker placements, and therapy delivery. Its compact size allows for use in both standard cylindrical and open configuration MRI scanners. Preliminary in vivo canine experiments and first clinical trials are reported.
Open Access
Evidence from autoimmune thyroiditis of skewed X-chromosome inactivation in female predisposition to autoimmunity
(Nature Publishing Group, 2006) Ozcelik, T.; Uz, E.; Akyerli, C. B.; Bagislar, S.; Mustafa, C. A.; Gursoy, A.; Akarsu, N.; Toruner, G.; Kamel, N.; Gullu, S.
The etiologic factors in the development of autoimmune thyroid diseases (AITDs) are not fully understood. We investigated the role of skewed X-chromosome inactivation (XCI) mosaicism in female predisposition to AITDs. One hundred and ten female AITDs patients (81 Hashimoto's thyroiditis (HT), 29 Graves' disease (GD)), and 160 female controls were analyzed for the androgen receptor locus by the HpaII/polymerase chain reaction assay to assess XCI patterns in DNA extracted from peripheral blood cells. In addition, thyroid biopsy, buccal mucosa, and hair follicle specimens were obtained from five patients whose blood revealed an extremely skewed pattern of XCI, and the analysis was repeated. Skewed XCI was observed in DNA from peripheral blood cells in 28 of 83 informative patients (34%) as compared with 10 of 124 informative controls (8% P<0.0001). Extreme skewing was present in 16 patients (19%), but only in three controls (2.4% P<60;0.0001). The buccal mucosa, and although less marked, the thyroid specimens also showed skewing. Analysis of two familial cases showed that only the affected individuals demonstrate skewed XCI patterns. Based on these results, skewed XCI mosaicism may play a significant role in the pathogenesis of AITDs.
Open Access
Frequent demonstration of human herpesvirus 8 (HHV-8) in bone marrow biopsy samples from Turkish patients with multiple myeloma (MM)
(Nature Publishing, 2001) Beksac, M.; Ma, M.; Akyerli, C.; DerDanielian, M.; Zhang, L.; Liu, J.; Arat, M.; Konuk, N.; Koc, H.; Ozcelik, T.; Vescio, R.; Berenson, J. R.
In order to investigate the frequency of HHV-8 in MM patients from another geographic location, we obtained fresh bone marrow (BM) biopsies from Turkish patients with MM (n = 21), monoclonal gammopathy of undetermined significance (MGUS) (n = 2), plasmacytoma (n = 1) with BM plasma cell infiltration, various hematological disorders (n = 6), and five healthy Turkish controls. The frequency of HHV-8 was analyzed by polymerase chain reaction (PCR) in two independent laboratories in the USA and in Turkey. Using fresh BM biopsies, 17/21 MM patients were positive for HHV-8 whereas all five healthy controls, and six patients with other hematological disorders were negative. Two patients with MGUS, and one patient with a solitary plasmacytoma were also negative. The data from the two laboratories were completely concordant. Also using primer pairs for v IRF and v IL-8R confirmed the results observed with the KS330233 primers. Furthermore, sequence analysis demonstrated a C3 strain pattern in the ORF26 region which was also found in MM patients from the US. Thus, HHV-8 is present in the majority of Turkish MM patients, and the absence of the virus in healthy controls further supports its role in the pathogenesis of MM.
Open Access
A hybrid classification model for digital pathology using structural and statistical pattern recognition
(Institute of Electrical and Electronics Engineers, 2013) Ozdemir, E.; Gunduz-Demir, C.
Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and grading. In this paper, we introduce an effective hybrid model that employs both structural and statistical pattern recognition techniques to locate and characterize the biological structures in a tissue image for tissue quantification. To this end, this hybrid model defines an attributed graph for a tissue image and a set of query graphs as a reference to the normal biological structure. It then locates key regions that are most similar to a normal biological structure by searching the query graphs over the entire tissue graph. Unlike conventional approaches, this hybrid model quantifies the located key regions with two different types of features extracted using structural and statistical techniques. The first type includes embedding of graph edit distances to the query graphs whereas the second one comprises textural features of the key regions. Working with colon tissue images, our experiments demonstrate that the proposed hybrid model leads to higher classification accuracies, compared against the conventional approaches that use only statistical techniques for tissue quantification. © 2012 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
Localization of diagnostically relevant regions of interest in whole slide images
(IEEE, 2014-08) Mercan, E.; Aksoy, Selim; Shapiro, L. G.; Weaver, D. L.; Brunye, T.; Elmore, J. G.
Whole slide imaging technology enables pathologists to screen biopsy images and make a diagnosis in a digital form. This creates an opportunity to understand the screening patterns of expert pathologists and extract the patterns that lead to accurate and efficient diagnoses. For this purpose, we are taking the first step to interpret the recorded actions of world-class expert pathologists on a set of digitized breast biopsy images. We propose an algorithm to extract regions of interest from the logs of image screenings using zoom levels, time and the magnitude of panning motion. Using diagnostically relevant regions marked by experts, we use the visual bag-of-words model with texture and color features to describe these regions and train probabilistic classifiers to predict similar regions of interest in new whole slide images. The proposed algorithm gives promising results for detecting diagnostically relevant regions. We hope this attempt to predict the regions that attract pathologists' attention will provide the first step in a more comprehensive study to understand the diagnostic patterns in histopathology.
Open Access
Localization of diagnostically relevant regions of interest in whole slide images: a comparative study
(Springer New York LLC, 2016-08) Mercan, E.; Aksoy, S.; Shapiro, L. G.; Weaver, D. L.; Brunyé, T. T.; Elmore, J. G.
Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors. © 2016, Society for Imaging Informatics in Medicine.
Open Access
Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data
(2007) Woods, B.J.; Clymer, B.D.; Kurc, T.; Heverhagen J.T.; Stevens, R.; Orsdemir, A.; Bulan O.; Knopp, M.V.
Purpose: To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods: 4D texture analysis was performedon DCE-MRI data sets of breast lesions. A model-free neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results: The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with α = 0.05. The results show that an area under the ROC curve (Az) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion: This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted. © 2007 Wiley-Liss, Inc.
Open Access
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
(Elsevier BV, 2009-06) Tosun, A. B.; Kandemir, M.; Sokmensuer, C.; Gunduz Demir, C.
Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis and detection of cancer. To overcome this problem, unlike previous methods that use pixel distributions, we propose a new homogeneity measure based on the distribution of the objects that we define to represent tissue components. Using this measure, we demonstrate a new object-oriented segmentation algorithm. Working with colon biopsy images, we show that this algorithm segments the cancerous and normal regions with 94.89 percent accuracy on the average and significantly improves the segmentation accuracy compared to its pixel-based counterpart. © 2008 Elsevier Ltd. All rights reserved.
Open Access
System for prostate brachytherapy and biopsy in a standard 1.5 T MRI scanner
(John Wiley & Sons, 2004) Susil, R.C.; Camphausen, K.; Choyke, P.; McVeigh, E. R.; Gustafson, G. S.; Ning, H.; Miller, R. W.; Atalar, Ergin; Coleman, C. N.; Ménard, C.
A technique for transperineal high-dose-rate (HDR) prostate brachytherapy and needle biopsy in a standard 1.5 T MRI scanner is demonstrated. In each of eight procedures (in four patients with intermediate to high risk localized prostate cancer), four MRI-guided transperineal prostate biopsies were obtained followed by placement of 14-15 hollow transperineal catheters for HDR brachytherapy. Mean needle-placement accuracy was 2.1 mm, 95% of needle-placement errors were less than 4.0 mm, and the maximum needle-placement error was 4.4 mm. In addition to guiding the placement of biopsy needles and brachytherapy catheters, MR images were also used for brachytherapy treatment planning and optimization. Because 1.5 T MR images are directly acquired during the interventional procedure, dependence on deformable registration is reduced and online image quality is maximized.
Open Access
Transrectal prostate biopsy and fiducial marker placement in a standard 1.5T magnetic resonance imaging scanner
(Elsevier Inc., 2006-01) Susil, R. C.; Menard, C.; Kriegel, A.; Coleman, J. A.; Camphausen, K.; Choyke, P.; Fichtinger, G.; Whitcomb, L. L.; Coleman, C. N.; Atalar, Ergin
Purpose: We investigated the accuracy and feasibility of a system that provides transrectal needle access to the prostate concurrent with 1.5 Tesla MRI which previously has not been possible. Materials and Methods: In 5 patients with previously diagnosed prostate cancer, MRI guided intraprostatic placement of gold fiducial markers (4 procedures) and/or prostate biopsy (3 procedures) was performed using local anesthesia. Results: Mean procedure duration was 76 minutes and all patients tolerated the intervention well. Procedure related adverse events included self-limited hematuria and hematochezia following 3 of 8 procedures (all resolved in less than 1 week). Mean needle placement accuracy was 1.9 mm for the fiducial marker placement studies and 1.8 mm for the biopsy procedures. Mean fiducial marker placement accuracy was 4.8 mm and the mean fiducial marker placement accuracy transverse to the needle direction was 2.6 mm. All patients who underwent the procedure were able to complete their course of radiotherapy without delay or complication. Conclusions: While studies of clinical usefulness are warranted, transrectal 1.5 T MRI guided prostate biopsy and fiducial marker placement is feasible using this system, providing new opportunities for image guided diagnostic and therapeutic prostate interventions.