Search

Objectives: To evaluate whether a follow‐up magnetic resonance imaging (MRI) scan performed after initial ultrasound (US) to evaluate soft tissue mass (STM) lesions of the musculoskeletal system provides additional radiologic diagnostic information and alters clinical management. Methods: A retrospective chart review was performed of patients undergoing initial US evaluations of STMs of the axial or appendicular skeleton between November 2012 and March 2019. Patients who underwent US examinations followed by MRI for the evaluation of STM lesions were identified. For inclusion, the subsequent pathologic correlation was required from either a surgical or image‐guided biopsy. Imaging studies with pathologic correlations were then reviewed by 3 musculoskeletal radiologists, who were blinded to the pathologic diagnoses. The diagnostic utility of MRI was then assessed on the basis of a 5‐point grading scale, and inter‐reader agreements were determined by the Fleiss κ statistic. Results: Ninety‐two patients underwent MRI after US for STM evaluations. Final pathologic results were available in 42 cases. Samples were obtained by surgical excision or open biopsy (n = 34) or US‐guided core biopsy (n = 8). The most common pathologic diagnoses were nerve sheath tumors (n = 9), lipomas (n = 5), and leiomyomas (n = 5). Imaging review showed that the subsequent MRI did not change the working diagnosis in 73% of cases, and the subsequent MRI was not considered to narrow the differential diagnosis in 68% of cases. There was slight inter‐reader agreement for the diagnostic utility of MRI among individual cases (κ = 0.10) between the 3 readers. Conclusions: The recommendation of MRI to further evaluate STM lesions seen with US frequently fails to change the working diagnosis or provide significant diagnostic utility. [ABSTRACT FROM AUTHOR]

Résumé: Nous rapportons le cas d'une patiente présentant un accident vasculaire cérébral (AVC) ischémique, secondaire à un thrombus de la carotide interne, sur plaque d'athérome. La taille du thrombus et son association à une embolie pulmonaire sont inhabituels et le seul facteur favorisant potentiel identifié est le contexte de Covid-19, sans signe de gravité de l'infection. La survenue de volumineuses thromboses artérielles au cours de la maladie liée au coronavirus est largement rapportée depuis le début de l'épidémie. Ce phénomène, responsable notamment d'AVC ischémiques sévères, ne survient pas nécessairement chez des patients présentant une forme grave de l'infection, mais semble associé à un moins bon pronostic fonctionnel. We report a case of ischemic stroke characterized by unusual large thrombosis of the internal carotid, origin associated to pulmonary embolism in a patient without any prothrombotic factor except from mild signs of Covid-19. The high incidence of thrombotic phenomens observed among patients with coronavirus during the last months does not seem to systematically occur among patients with severe presentation of the infection, but seems associated to a worst functional prognosis. [ABSTRACT FROM AUTHOR]

Purpose: To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses., Materials and Methods: In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision ( n = 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation ( n = 192) were included. Images in patients with a histologic diagnosis ( n = 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history. A second group of US images from 275 of the 419 patients containing the three common benign masses was used to train and evaluate a separate model to differentiate between the masses. The models were trained on the Keras machine learning platform (version 2.3.1), with a modified pretrained VGG16 network. Performance metrics of the model and of the radiologists were compared by using the McNemar test, and 95% CIs for performance metrics were estimated by using the Clopper-Pearson method (accuracy, recall, specificity, and precision) and the DeLong method (area under the receiver operating characteristic curve)., Results: The model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data., Conclusion: The trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists.© RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: B.W. disclosed no relevant relationships. L.P. Activities related to the present article: grant from the French Agence régionale de sante” for a volunteer research internship in NYU Langone. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. C.B. disclosed no relevant relationships. R.S.A. disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)