Sarcomatoid urothelial carcinoma accompanied by elevated serum beta-HCG: Two case reports

Sarcomatoid urothelial carcinoma is a rare and aggressive variant. Serum beta-hCG levels are used as a tumor marker in gestational trophoblastic diseases and germ cell tumors, but may also be elevated in high-grade bladder cancers. Here, we report two urothelial carcinoma cases with sarcomatoid differentiation that relapsed early after surgery with elevated serum beta-hCG levels. The first case was a 65-year-old female and the second case was a 67-year-old man with sarcomatoid urothelial carcinoma located in the ureter and renal pelvicalyceal system, both of them relapsed with elevated beta-hCG serum level to 146.8 mIU/ mL and 242 mIU/mL, respectively. They died a few months after initial diagnosis;4.9 and 2.5 months respectively. Both sarcomatoid variant and beta-hCG expression were associated with poor prognosis and advanced stage. However, beta-hCG is not used as a tumor marker in urinary tract cancers yet, and its relationship with variant pathologies has not been clarified. We need multi-centered studies to reveal this relationship.Copyright © 2023, Derman Medical Publishing. All rights reserved.

Is histogram analysis useful in the diagnosis of COVID-19 patients?

Aim: In this study, we aimed to show the contribution of the chest computed tomography (CT)-based histogram analysis method, which will enable us to make quick decisions for patients who are clinically suspected of having COVID-19 infection and whose diagnoses cannot be confirmed by polymerase chain reaction (PCR) tests. Material(s) and Method(s): A total of 84 patients, 40 in the PCR-positive group (age range: 17-90 years) and 44 in the PCR-negative group (age range: 15-75 years), were included in the study. A total of 154 lesions with ground-glass density, 78 in the PCR-positive group and 76 in the PCR-negative group, were detected in these patients' thorax CT scans. The region of interest was placed on the ground-glass opacities from the images and numerical data were obtained by histogram analysis. Numerical data were uploaded to the MATLAB program. Result(s): The localizations of ground-glass densities in the CT findings of patients with probable and definite COVID-19 diagnoses were similar;74.7% of the ground-glass areas in both groups showed peripheral distribution. Lesions were frequently observed in right lungs and lower lobes. In histogram analysis, standard deviation, variance, size %L, size %M, and kurtosis values were higher in the PCR-positive than the PCR-negative group. When receiver operating characteristic curve analysis was performed for standard deviation values, the area under the curve was 0.640, and when the threshold value was selected as 123.4821, the two groups could be differentiated with 62.8% sensitivity and 61.8% specificity. Discussion(s): The use of histogram-based tissue analysis, which is a subdivision of artificial intelligence, for clinically highly suspicious patients increases the diagnostic accuracy of CT. Therefore, performing CT analysis with the histogram method will significantly aid healthcare professionals, especially in clinics where rapid decisions are required, such as in emergency services.Copyright © 2022, Derman Medical Publishing. All rights reserved.

A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has been used to detect the virus. However, since it is an expensive and slow method, artificial intelligence researchers have attempted to develop quick, inexpensive alternative methods of diagnosis to help doctors identify positive cases. Therefore, researchers are starting to incorporate chest X-ray scans (CXRs), an easy and inexpensive examination method. This study used an approach that uses image cropping methods and a deep learning technique (updated VGG16 model) to classify three public datasets. This study had four main steps. First, the data were split into training and testing sets (70% and 30%, respectively). Second, in the image processing step, each image was cropped to show only the chest area. The images were then resized to 150 × 150. The third step was to build an updated VGG16 convolutional neural network (VGG16-CNN) model using multiple classifications (three classes: COVID-19, normal, and pneumonia) and binary classification (COVID-19 and normal). The fourth step was to evaluate the model’s performance using accuracy, sensitivity, and specificity. This study obtained 97.50% accuracy for multiple classifications and 99.76% for binary classification. The study also got the best COVID-19 classification accuracy (99%) for both models. It can be considered that the scientific contribution of this research is summarized as: the VGG16 model was reduced from approximately 138 million parameters to around 40 million parameters. Further, it was tested on three different datasets and proved highly efficient in performance. © 2022 by the authors.

Is histogram analysis useful in the diagnosis of COVID-19 patients?

Aim: In this study, we aimed to show the contribution of the chest computed tomography (CT)-based histogram analysis method, which will enable us to make quick decisions for patients who are clinically suspected of having COVID-19 infection and whose diagnoses cannot be confirmed by polymerase chain reaction (PCR) tests. Material and Methods: A total of 84 patients, 40 in the PCR-positive group (age range: 17-90 years) and 44 in the PCR-negative group (age range: 15-75 years), were included in the study. A total of 154 lesions with ground-glass density, 78 in the PCR-positive group and 76 in the PCR-negative group, were detected in these patients' thorax CT scans. The region of interest was placed on the ground-glass opacities from the images and numerical data were obtained by histogram analysis. Numerical data were uploaded to the MATLAB program. Results: The localizations of ground-glass densities in the CT findings of patients with probable and definite COVID-19 diagnoses were similar;74.7% of the ground-glass areas in both groups showed peripheral distribution. Lesions were frequently observed in right lungs and lower lobes. In histogram analysis, standard deviation, variance, size %L, size %M, and kurtosis values were higher in the PCR-positive than the PCR-negative group. When receiver operating characteristic curve analysis was performed for standard deviation values, the area under the curve was 0.640, and when the threshold value was selected as 123.4821, the two groups could be differentiated with 62.8% sensitivity and 61.8% specificity. Discussion: The use of histogram-based tissue analysis, which is a subdivision of artificial intelligence, for clinically highly suspicious patients increases the diagnostic accuracy of CT. Therefore, performing CT analysis with the histogram method will significantly aid healthcare professionals, especially in clinics where rapid decisions are required, such as in emergency services.