Introduction: Chest CT is thought to be sensitive but less specific in diagnosing the 2019 coronavirus disease . The diagnostic value of CT is unclear. We aimed to compare the performance of CT and initial RT-PCR for clinically suspected COVID-19 patients outside the epicentre-Wuhan, China. Materials and methods: Patients clinically suspected of COVID-19 infection who underwent initial RT-PCR and chest CT at the same time were retrospectively enrolled. Two radiologists with specific training reviewed the CT images independently and final diagnoses of the presence or absence of COVID-19 was reached by consensus. With serial RT-PCR as reference standard, the performance of initial RT-PCR and chest CT was analysed. A strategy of combining initial RT-PCR and chest CT was analysed to study the additional benefit. Results: 82 patients admitted to hospital between Jan 10, 2020 to Feb 28, 2020 were enrolled. 34 COVID-19 and 48 non-COVID-19 patients were identified by serial RT-PCR. The sensitivity, specificity was 79% (27/34) and 100% (48/48) for initial RT-PCR and 77% (26/34) and 96% (46/48) for chest CT. The image readers had a good interobserver agreement with Cohen's kappa of 0.69. No statistical difference was found in the diagnostic performance between initial RT-PCR and chest CT. The comprehensive strategy had a higher sensitivity of 94% (32/ 34). Conclusions: Initial RT-PCR and chest CT had comparable diagnostic performance in identification of suspected COVID-19 patients outside the epidemic center. To compensate potential risk of false-negative PCR, chest CT should be applied for clinically suspected patients with negative initial RT-PCR.

Background: Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis. Methods: Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/− consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19). Results: Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score > 2. Conclusions: With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

PurposeTo establish and verify a predictive model involving multiparameter MRI and clinical manifestations for predicting synchronous lung metastases (SLM) in osteosarcoma.Materials and MethodsSeventy-eight consecutive patients with osteosarcoma (training dataset, n = 54; validation dataset, n = 24) were enrolled in our study. MRI features were extracted from the T1‐weighted image (T1WI), T2‐weighted image (T2WI), and contrast-enhanced T1-weighted image (CE-T1WI) of each patient. Least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression were performed to select key features and build radiomics models in conjunction with logistic regression (LR) and support vector machine (SVM) classifiers. Eight individual models based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, T1WI+T2WI+CE-T1WI, and clinical features, as well as two combined models, were built. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were employed to assess the different models.ResultsTumor size was the most significant univariate clinical indicator (1). The AUC values of the LR predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.686, 0.85, 0.87, 0.879, 0.736, 0.85, and 0.914, respectively (2). The AUC values of the SVM predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI +CE-T1WI, T2WI +CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.629, 0.829, 0.771, 0.879, 0.643, 0.829, and 0.929, respectively (3). The AUC values of the clinical, combined 1 (clinical and LR-radiomics) and combined 2 (clinical and SVM-radiomics) predictive models were 0.779, 0.957, and 0.943, respectively.ConclusionThe combined model exhibited good performance in predicting osteosarcoma SLM and may be helpful in clinical decision-making.

Objective: This study aims to assess the CT and MRI features of head and neck osteosarcoma (HNO). Methods: 37 HNOs were identified, and the following imaging characteristics were reviewed on CT and MRI. Results: A total of 37 patients(age 41.5 ± 15.0 years old; 16 males, 21 females) were included in the study. Tumours occurred in the maxilla (16, 43.2%), mandible (8, 21.6%), skull base (6, 16.2%), calvarium (5, 13.5%), paranasal sinuses (1, 2.7%) and cervical soft tissue (1, 2.7%). 16 patients received radiotherapy for nasopharyngeal carcinoma. Three patients (8.1%) developed osteosarcomas related to a primary bone disease. 16 of the (43.2%) tumours demonstrated lytic density on CT scans, followed by 13 (35.1%) showing mixed density and 7 (18.9%) with sclerotic density. Matrix mineralization was present in 32 (86.5%). 3 out of 24 (12.5%) tumours showed lamellar periosteal reactions, 21 out of 24 (87.5%) showed spiculated periosteal reactions. 12 tumours showed low signal intensities on T1WI, with 16 having heterogeneous signal intensities. 10 tumours showed high signal intensities on T2WI, and 18 showed heterogeneous signal intensities. With contrast-enhanced images, 3 tumours showed homogeneous enhancement (2 osteoblastic and 1 giant cell-rich), 18 tumours showed heterogeneous enhancement (13 osteoblastic, 4 fibroblastic and 1 giant cell-rich), and 7 tumours showed peripheral enhancement (6 chondroblastic and 1 osteoblastic). These tumours were characterized by soft tissue masses with a diameter of 5.6 ± 1.8 cm. Conclusions: HNO is a rare condition and is commonly associated with previous radiation exposure. This study provides age, sex distribution, location, CT and MRI features of HNO.

Objective: This study aims to assess the CT and MRI features of calvarium and skull base osteosarcoma (CSBO). Methods: The CT and MRI features and pathological characteristics of 12 cases of pathologically confirmed CSBO were analyzed retrospectively. Results: 12 patients (age range 9–67 years; 3 male, 9 female) were included in the study. Tumours occurred in skull base (7, 58.3%), temporal (4, 33.3%) and frontal (1, 8.3%). Among all, six patients received radiotherapy for nasopharyngeal carcinoma. According to pathology, 11 out of 12 tumours were high-grade (91.7%). On CT, all the tumours had soft tissue mass penetrated into cortical bone with invasion of surrounding soft tissue. Six tumours were shown to have lytic density and six were mixed density. Matrix mineralization was present in 10 cases (83.3%). On MRI, tumours presented as soft-tissue masses measuring 5.9 ± 2.4 (3.9–8.0) cm. Five tumours showed low signal intensities on T1 weighted imaging with seven having heterogeneous signal intensities. One showed low signal intensity on T2 weighted imaging, two showed high signal intensities and nine heterogeneous signal intensities. All the tumours showed low signal intensities on diffusion-weighted imaging. On contrast enhanced images, seven cases showed heterogeneous enhancement, three showed peripheral enhancementand and two showed homogeneous enhancement. Dural tail sign were detected in nine cases. Conclusion: CSBO is rare, and is commonly associated with previous radiation exposure. A presumptive diagnosis for osteosarcoma should be considered when calvarium and skull base tumours with osteoid matrix and duraltail sign are found. Advances in knowledge: CT and MR features of CSBO have not been reported. The study helps to identify CSBO and other sarcomas.