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Aims: To assess the diagnostic performance of digital breast tomosynthesis (DBT) in older women across varying breast densities and to compare its effectiveness for cancer detection with 2D mammography and ultrasound (U/S) for different breast density categories. Furthermore, our study aimed to predict the potential reduction in unnecessary additional examinations among older women due to DBT., Methods: This study encompassed a cohort of 224 older women. Each participant underwent both 2D mammography and digital breast tomosynthesis examinations. Supplementary views were conducted when necessary, including spot compression and magnification, ultrasound, and recommended biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated for 2D mammography, DBT, and ultrasound. The impact of DBT on diminishing the need for supplementary imaging procedures was predicted through binary logistic regression., Results: In dense breast tissue, DBT exhibited notably heightened sensitivity and NPV for lesion detection compared to non-dense breasts (61.9% vs. 49.3%, p < 0.001) and (72.9% vs. 67.9%, p < 0.001), respectively. However, the AUC value of DBT in dense breasts was lower compared with non-dense breasts (0.425 vs. 0.670). Regarding the ability to detect calcifications, DBT demonstrated significantly improved sensitivity and NPV in dense breasts compared to non-dense breasts (100% vs. 99.2%, p < 0.001) and (100% vs. 94.7%, p < 0.001), respectively. On the other hand, the AUC value of DBT was slightly lower in dense breasts compared with non-dense (0.682 vs. 0.711). Regarding lesion detection for all cases between imaging examinations, the highest sensitivity was observed in 2D mammography (91.7%, p < 0.001), followed by DBT (83.7%, p < 0.001), and then ultrasound (60.6%, p < 0.001). In dense breasts, sensitivity for lesion detection was highest in 2D mammography (92.9%, p < 0.001), followed by ultrasound (76.2%, p < 0.001), and the last one was DBT. In non-dense breasts, sensitivities were 91% ( p < 0.001) for 2D mammography, 50.7% ( p < 0.001) for ultrasound, and 49.3% ( p < 0.001) for DBT. In terms of calcification detection, DBT displayed significantly superior sensitivity compared to 2D mammography in both dense and non-dense breasts (100% vs. 91.4%, p < 0.001) and (99.2% vs. 78.5%, p < 0.001), respectively. However, the logistic regression model did not identify any statistically significant relationship ( p > 0.05) between DBT and the four dependent variables., Conclusion: Our findings indicate that among older women, DBT does not significantly decrease the requirement for further medical examinations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gharaibeh, Alfwares, Elobeid, Khasawneh, Rousan, El-Heis, Al-Jarrah, Haj Hussein, Altalhi and Abualigah.)

There has been a recent outbreak of monkeypox in the United States, mostly among men who have sex with men (MSM). It seems to be a self-limiting disease but can be potentially serious in immunocompromised patients. Monkeypox infection is transmitted mainly through skin-to-skin contact and possibly seminal and vaginal fluids. Very few cases of monkeypox infection in immunocompromised patients have been published in the literature. We report a case of infection in a renal transplant recipient, and describe the clinical course and outcome., Learning Points: There has been a recent outbreak of monkeypox infection in the United States and more studies are needed to assess the disease course in different patient populations.Monkeypox infection seems to have a similar clinical course in renal transplant recipients as in the general population.There is a higher risk of transmission among renal transplant recipients in men who have sex with men., Competing Interests: Conflicts of Interests: The Authors declare that there are no competing interests., (© EFIM 2023.)

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2022 Dalia Alzu'bi et al.)