The non-EPI MRI technique is a more accurate method in detecting middle ear cholesteatoma in comparison to other conventional sequences.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.

IntroductionA thyroglossal duct cyst is the most common congenital anomaly of the thyroid gland and midline masses in childhood (70% abnormality in childhood, 7% in adult). Carcinomas arising from a thyroglossal duct cyst are rare (only 1% of thyroglossal duct cyst cases) and characterized by relatively non-aggressive behavior and rare lymphatic spread. They are also diagnosed mostly during the third and fourth decades of life. About 85% to 92% of all thyroglossal duct cyst carcinomas are papillary carcinomas.Case presentationWe present the case of a 44-year-old Iranian woman with Cacausian ethnicity with a painless anterior neck mass that appeared gradually over three months. She had a history of frequent painful swelling of the anterior part of her neck, which subsided with antibiotic therapy. Thyroid functional tests were normal and a thyroid scinitigraphy showed a cold nodule in the left lobe of her thyroid. A computed tomography scan revealed a large, heterogeneous enhancing soft tissue mass with cystic components in the midline of the anterior neck space. This extended from the base of the tongue,(completely separated from its muscles, to the inferior aspect of the thyroid gland and showed the destruction of the hyoid bone and the thyroid cartilage. The diagnosis of a thyroglossal duct cyst with malignant transformation was maintained. A fine needle aspiration revealed papillary carcinoma.ConclusionThis patient's case is presented because of its rare, aggressive, and invasive nature and rare and unusual manifestation, as well as its rapid increase in size, the destruction of the hyoid bone, chondrolysis of the thyroid cartilage, lymph adenopathy and the existence of a cold nodule in the thyroid gland.

Objectives: Pre-operative differentiation of salivary gland neoplasms is of great importance. This study was designed to evaluate the use of dynamic contrast-enhanced MRI (DCE-MRI) for differentiation between malignant, Warthin and benign non-Warthin (BNW) neoplasms of major salivary glands. Methods: 46 major salivary gland tumours (SGTs) underwent pre-operative DCE-MRI. Postsurgical histopathological evaluation showed 30 BNW, 6 Warthin and 10 malignant tumours. Time-signal intensity curves (TICs) were categorized as (a) Tpeak .43 s and washout ratio at 180 s (WR180) ,4.6%; (b) Tpeak ,43 s and WR .22%; (c) Tpeak .43 s and WR180 5 4.6-22.0% Results: Accuracy of Tpeak was 98.9% for differentiation between BNW and Warthin tumours, 83.7% between BNW and malignant and 80% between malignant and Warthin tumours. All Warthin tumours showed Tpeak #43 s, while one BNW had Tpeak ,43 s. A Tpeak ,63.5 s differentiated 8/10 (80%) malignant tumours from BNW tumours, whereas 4/30 of BNW tumours had a Tpeak ,63.5 s. Two malignant tumours had Tpeak ,43 s. WR180 had an accuracy of 100% for differentiation between Warthin and BNW tumours, 87.3% between BNW and malignant, and 93.3% between Warthin and malignant tumours. 29 (96.7%) BNW tumours had a washout ,4.60%, while 8 (80%) malignant tumours had a washout .4.60%. All Warthin tumours had a WR180 .22%, while two malignant tumours had a WR180 .22%. 29/30 of BNW tumours demonstrated TIC curve Type A and 1 tumour demonstrated Type C. 6/10 of malignant tumours had TIC Type C, 2 had TIC Type A and 2 Type B. All Warthin tumours were categorized as Type B. Conclusions: This study showed that DCE-MRI could be helpful in pre-operative differentiation of SGTs; especially for discrimination between Warthin and BNW tumours.

Background: Chest computed tomography (CT) scan has been used widely to diagnose COVID-19 in Iran. Objectives: To trace the footsteps of COVID-19 in Iran by exploring the trend in using chest CT scans and its economic impact on radiology departments. Methods: In this cross-sectional study, the number of imaging examinations from 33 tertiary radiology departments in 9 large cities of Iran was collected from September 23, 2019 to March 20, 2020 (Months 1 to 6) and the corresponding months in 2018–2019. Results: A 50.2% increase was noted in the chest CT scan utilization in 2019–2020 compared to 2018–2019. This increase was +15%, +15%, +27%, +2%, +1% in Months 1–5 of 2019–2020, respectively. In Month 6 of 2019–2020, a 251% increase in the acquisition of chest CT scans was observed compared to the Month 6 of 2018–2019. Following negative balance of revenue from Month 1 to 5 with respect to the inflation rate, the total income in Month 6 was further 1.5% less than the same Month in 2018–19. Conclusion: The observed peak in chest CT utilization in Month 3 prior to the surge in Month 6 could be explained by the seasonal influenza. However, unawareness about an emerging viral disease, i.e. COVID-19, might have underutilized chest CT in Months 4 and 5 before the official announcement in Month 6. The unbalanced increase in the workload of radiology departments in the shortage of cardiothoracic radiologists with the simultaneous decrease in income initiated a vicious cycle that worsened the economic repercussions of the pandemic.