Background/aim: COVID-19 infection which started in Wuhan City, China in December 2019 turned into a pandemic in a very short time, affecting mainly the elderly and those with serious chronic illnesses. COVID-19 infection has been observed with a high mortality rate especially in patients undergoing maintenance haemodialysis. Materials and methods: Forty-two patients over 18 years of age, who underwent a maintenance haemodialysis program at our unit, and being tested positive for COVID-19 by PCR from nasopharyngeal swabs and/or were observed to have disease-related signs in their CTs were included in the study. Results:In this study, 23 of 42 patients receiving haemodialysis support in our clinic were included. The median age was 67 years (min 35; max 91 years) and all of them had primary hypertension and other comorbidities. Their clinical evaluation showed that dry cough (47.8%) and shortness of breath (47.8%) were the most common symptoms. Fever was less pronounced (30.4%). The median time from the onset of symptoms to hospitalization was 1 day (min 0; max) and time from hospitalization to death was 18 days (min:1; max 22). Transfer from the inpatient ward to ICU took a median of 7 days (min 1; max 13). Among the 23 patients, three died during the follow-up and 20 were discharged with full recovery. Baseline ferritin, procalcitonin levels and CRP/albumin rates higher and neutrophil/lymphocyte levels lower in patient who died. In these patients, despite being nonsignificant, there were more diabetic patients and D-dimer levels were higher than 1000 ugFEU/L. 2 Conclusion: COVID-19 infection is associated with increased mortalit in chronic kidney diseases patients. Despite being non-significant, there was a trend towards increased mortality in patient with diabetes, D-dimer levels >1000 ugFEU/L and higher ferritin, prokalsitonin levels, increased CRP/albumin raio and lower neutrophil/lymphocyte ratio.

No abstract

The presented study aims to design a computer-aided detection and diagnosis system for breast dynamic contrast enhanced magnetic resonance imaging. In the proposed system, the segmentation task is performed in two stages. The first stage is called breast region segmentation in which adaptive noise filtering, local adaptive thresholding, connected component analysis, integral of horizontal projection, and breast region of interest detection algorithms are applied to the breast images consecutively. The second stage of segmentation is breast lesion detection that consists of 32-class Otsu thresholding and Markov random field techniques. Histogram, gray level co-occurrence matrix and neighboring gray tone difference matrix based feature extraction, Fisher score based feature selection and, tenfold and leave-one-out cross-validation steps are carried out after segmentation to increase the reliability of the designed system while decreasing the computational time. Finally, support vector machines, k-nearest neighbor, and artificial neural network classifiers are performed to separate the breast lesions as benign and malignant. The average accuracy, sensitivity, specificity, and positive predictive values of each classifier are calculated and the best results are compared with the existing similar studies. According to the achieved results, the proposed decision support system for breast lesion segmentation distinguishes the breast lesions with 86%, 100%, 67%, and 85% accuracy, sensitivity, specificity, and positive predictive values, respectively. These results show that the proposed system can be used to support the radiologists during a breast cancer diagnosis.

No abstract

Breast cancer is one of the most common cancer types especially met in women. The number of breast cancer patients increases every year. Thus, to detect breast cancer at its early stages gains importance. Breast region detection is the pioneering step of breast cancer diagnosis researches performed via image processing techniques. The performance of computer-aided breast cancer diagnosis systems can be improved by exactly determining the breast region of interest. In this study, the goal is to determine a region of interest for breast MR images, in which one or more lesion can appear. The achieved region includes two breasts and lymph nodes. The proposed region of interest detection system is fully automatic and it utilizes several image processing techniques. At first, the local adaptive thresholding technique is applied to the noise-filtered grey level breast magnetic resonance images taken with ethical permissions from Sakarya Education and Research Hospital. After adaptive thresholding, connected component analysis is performed to exclude extra structures around the breast region as thorax area. This analysis selects the largest area in the binary image which corresponds to a gyrate region including breast area and lymph nodes over the backbone. Then, the integral of horizontal projection is calculated to determine an optimum horizontal line that allows setting the region of interest apart. In the following step, sternum midpoint is detected to separate the right breast from the left one. Finally, a masking operation is applied to get corresponding right and left breast regions in the original MR image. To evaluate the performance of the proposed study, the results of automatic region of interest detection system are compared with the manual region of interest selection performed by an expert radiologist. Dice similarity coefficient and Jaccard coefficient are used as performance criteria. According to the results, the proposed system can detect region of interest for computer-aided breast cancer diagnosis researches, exactly.