On CT, LR-5 category has near-perfect specificity for the diagnosis of HCC and ancillary features modifies the final category in few observations.

Objectives: To understand and report the perceived impact of the COVID-19 pandemic on radiology residents in Saudi Arabia with respect to their education, clinical activities, and personal well-being. Methods:The survey questionnaire was designed by a team of experts based on a review of the literature and was distributed electronically through the Saudi Commission for Health Specialties to residents registered in all radiology residency training programs in Saudi Arabia during the academic year 2019 to 2020. Categorical variables were presented as counts and percentage. Numerical variables were presented as mean and standard deviation if normally distributed. Chi-square testing was used to compare categorical variables with the perceived impact of the COVID-19 pandemic. Spearman correlation was used to correlate numerical variables at the level of significance p-value < 0.05.Results: A total of 109 residents completed the online survey during the study period, with a response rate of 32.2% (109/337). The mean age was 27.3 years (standard deviation, 1.86). The majority of respondents (71.5%, 78/109) reported either a severe or moderate negative impact on educational activities. Also, the majority (73.4%, 80/109) reported either a minimal or moderate negative impact on clinical activities. Residents training in the western province perceived a statistically higher negative impact on educational activities compared to their peers in other regions (p = 0.01). Residents in their second year of residency training perceived a statistically higher negative impact on their participation in clinical activities (p = 0.014). Less than half of the respondents (45.9%, 50/109) reported that they were redeployed to work in another department. The majority (80%, 40/50) reported a negative impact on their well-being. Conclusion:The majority of radiology residents in Saudi Arabia reported a negative impact of the COVID-19 pandemic on their education, clinical activities, and personal well-being. Our study also identified and explored some of the innovative solutions and strategies implemented by the training programs and the SCFHS to mitigate the negative effects on trainees.

In neuro-oncology, magnetic resonance imaging (MRI) is a critically important, non-invasive radiologic assessment technique for brain tumor diagnosis, especially glioma. Deep learning improves MRI image characterization and interpretation through the utilization of raw imaging data and provides unprecedented enhancement of images and representation for detection and classification through deep neural networks. This systematic review and quality appraisal method aim to summarize deep learning approaches used in neuro-oncology imaging to aid healthcare professionals. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a total of 20 low-risk studies on the established use of deep learning models to identify glioma genetic mutations and grading were selected, based on a Quality Assessment of Diagnostic Accuracy Studies 2 score of ≥9. The included studies provided the deep learning models used alongside their outcome measures, the number of patients, and the molecular markers for brain glioma classification. In 19 studies, the researchers determined that the deep learning model improved the clinical outcome and treatment protocol in patients with a brain tumor. In five studies, the authors determined the sensitivity of the deep learning model used, and in four studies, the authors determined the specificity of the models. Convolutional neural network models were used in 16 studies. In eight studies, the researchers examined glioma grading by using different deep learning models compared with other models. In this review, we found that deep learning models significantly improve the diagnostic and classification accuracy of brain tumors, particularly gliomas without the need for invasive methods. Most studies have presented validated results and can be used in clinical practice to improve patient care and prognosis.

Background Liver abscesses differ in their aetiology, location, and number. Image‐guided percutaneous drainage techniques are the currently used management for liver abscesses. We conducted our study to compare the clinical safety and efficacy of percutaneous needle aspiration (PNA) to percutaneous catheter drainage (PCD). Methods A systematic review of major reference databases was undertaken in February 2022 for randomized controlled trials (RCTs) that compare PNA to PCD in treating liver abscess patients. The quality of the included trials was assessed using the Cochrane tool. Statistical meta‐analysis was conducted using RevMan and open meta‐analyst software. Results Fifteen RCTs were included in this review, with 1676 patients enrolled. The overall quality of the included trials was moderate, with most domains of unclear risk. PCD was superior to PNA in the success rate (RR = 1.23; 95% CI [1.12, 1.36], P < 0.00001), time for achieving 50% reduction of cavity size (MD = −2.32; 95% CI [−3.07, −1.57], P < 0.00001), and time for clinical improvement (MD = −1.92; 95% CI [−2.55, −1.28], P < 0.00001). The two modalities did not differ in the days of hospital stay, duration of IV antibiotics, and time needed for total or subtotal reduction of cavity size (P = 0.36, P = 0.06 and P = 0.40, respectively). High heterogeneity levels were detected. Regarding major complications, the two modalities were equally safe (P = 0.39). Conclusion PCD has a higher success rate and results in a faster 50% reduction in the abscess cavity size and clinical improvement. The two modalities are equally safe.