Search

Background: The Radiologic Society of North America (RSNA) divides patients into four sections: negative, atypical, indeterminate, and typical coronavirus disease 2019 (COVID-19) pneumonia based on their computed tomography (CT) scan findings. Herein, we evaluate the frequency of the chest CT-scan appearances of COVID-19 according to each RSNA categorical group. Methods: A total of 90 patients with real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 were enrolled in this study and differences in age, sex, cardiac characteristics, and imaging features of lung parenchyma were evaluated in different categories of RSNA classification. Results: According to the RSNA classification 87.8, 5.56, 4.44, and 2.22% of the patients were assigned as typical, indeterminate, atypical, and negative, respectively. The proportion of "atypical" patients was higher in the patients who had mediastinal lymphadenopathy and pleural effusion. Moreover, ground-glass opacity (GGO) and consolidation were more pronounced in the lower lobes and left lung compared to the upper lobes and right lung, respectively. While small nodules were mostly seen in the atypical group, small GGO was associated with the typical group, especially when it is present in the right lung and indeterminate group. Conclusion: Regardless of its location, non-round GGO is the most prevalent finding in the typical group of the RSNA classification systems. Mediastinal lymphadenopathy, pleural effusion, and small nodules are mostly observed in the atypical group and small GGO in the right lung is mostly seen in the typical group. [ABSTRACT FROM AUTHOR]

Objectives: Endometriosis is a significant cause of chronic abdominal pain and infertility in females, often overlooked due to its resemblance to other abdominopelvic pathologies. This study aims to compare the diagnostic performance and agreement rate between an abbreviated MRI protocol (aMRI) and a full MRI protocol (fMRI) for detecting pelvic endometriosis., Methods: We retrospectively analysed 446 consecutive MRI exams, including both full (fMRI) and abbreviated (aMRI) protocols, performed for suspected pelvic endometriosis. An expert radiologist assessed the presence of endometriosis at 14 distinct anatomical sites. Each MRI protocol was interpreted in random order, with a minimum two-week interval between sessions to minimize recall bias. Agreement between the protocols was evaluated using kappa statistics., Results: The average age of the patients was 34.13 years. The highest incidences of endometriosis were found in the ovaries (88.8%) and the recto-uterine pouch (65%). The MRI protocols demonstrated perfect agreement (kappa coefficient = 1) for the ovaries, bladder, uterus, and caesarean section scar. High agreement was also observed in the rectum and uterine ligaments (kappa coefficients of 0.98 and 0.97). Detection of malignant transformation in existing ovarian endometriomas showed substantial concordance with a kappa coefficient of 0.66., Conclusions: An abbreviated non-contrast MRI protocol exhibits diagnostic accuracy comparable to that of a comprehensive protocol in detecting pelvic endometriosis, with similar confidence and reproducibility., Advances in Knowledge: This study demonstrates that an abbreviated MRI protocol is as effective as a full protocol in diagnosing pelvic endometriosis, potentially allowing for quicker, cost-effective imaging without compromising diagnostic accuracy., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Background: Developmental dysplasia of the hip (DDH) is used to describe a spectrum of structural abnormalities that involve the growing hip. Early diagnosis and treatment are critical to providing the best possible functional outcome. This study aimed to evaluate the prevalence of DDH in neonates with and without risk factors and determine the role of ultrasound screening on the initial diagnosis., Methods: This prospective cross-sectional study was conducted on 399 infants at the Pediatric Treatment Center, Tehran University of Medical Sciences, between December 2015 and June 2016. Infants with suspected DDH who underwent hip ultrasonography were included, and the presence or absence of each risk factor was documented according to the checklist. The ultrasound findings were also registered in the checklists. The odds ratio (OR) of each risk factor for DDH was calculated. The collected data were analyzed by SPSS software version 18 at a 0.05 significance level., Results: In 16 months of study, 174 (43.6%) male and 225 (56.4%) female infants under the age of 18 months were studied. Risk factors were detected in the medical history of 329 infants. Out of them, 230(57.6%) were firstborn children, 7 (1.75%) had a positive family history of DDH, and 26 (6.5%) had limb anomalies. There was also a history of breech presentation in 16 (4.01%) and a history of oligohydramnios in 21 (5.1%) of infants. The prevalence of DDH was 25.8% in infants with risk factors and 2.8% in those without risk factors. (OR = 11.84, P < 0.05)., Conclusion: In this study, the frequency of DDH was significantly higher in infants with risk factors. The female gender and limb anomalies were stronger risk factors for DDH. Overall, ultrasound showed great potential for DDH screening., Competing Interests: The authors declare that they have no competing interests., (© 2024 Iran University of Medical Sciences.)

Aim: This study aimed to assess the effectiveness of using MRI-apparent diffusion coefficient (ADC) map-driven radiomics to differentiate between hepatocellular adenoma (HCA) and hepatocellular carcinoma (HCC) features., Materials and Methods: The study involved 55 patients with liver tumors (20 with HCA and 35 with HCC), featuring 106 lesions equally distributed between hepatic carcinoma and hepatic adenoma who underwent texture analysis on ADC map MR images. The analysis identified several imaging features that significantly differed between the HCA and HCC groups. Four classification models were compared for distinguishing HCA from HCC including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), random forest (RF), and k-nearest neighbor (KNN)., Results: The k-nearest neighbor (KNN) classifier displayed the top accuracy (0.89) and specificity (0.90). Linear-SVM and KNN classifiers showcased the leading sensitivity (0.88) for both, with the KNN classifier achieving the highest precision (0.9). In comparison, the conventional interpretation had lower sensitivity (70.1%) and specificity (77.9%)., Conclusion: The study found that utilizing ADC maps for texture analysis in MR images is a viable method to differentiate HCA from HCC, yielding promising results in identified texture features., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Abdullah et al.)