Background: Chest CT is used for the assessment of the severity of patients infected with novel coronavirus 2019 (COVID-19). We collected chest CT scans of 202 patients diagnosed with the COVID-19, and try to develop a rapid, accurate and automatic tool for severity screening follow-up therapeutic treatment.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. By taking the advantages of the pre-trained deep neural network, four pre-trained off-the-shelf deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) were exploited to extract the features from these CT scans. These features are then fed to multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, tenfold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines.Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models shows the promising application in COVID-19 severity screening, whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for tenfold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

Background: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severity of COVID-19 determines the management, treatment, and even prognosis. We aim to develop and validate a radiomics nomogram for identifying patients with severe COVID-19. Methods: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts, respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness. Results: The radiomics signature consisting of four selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19. Conclusion: We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.

Most melanomas identified in the stomach are metastatic; primary gastric melanoma (PGM) is extremely rare, and the relevant studies are relatively scarce. PGM may be incorrectly diagnosed as other gastric malignant tumor types. We describe a rare case of PGM confirmed through long-term clinical observation and pathological diagnosis. A 67-year-old woman presented to our hospital with recurrent chest tightness and chest pain. Digital gastrointestinal radiography revealed a circular shadow in the gastric cardia. Computed tomography (CT) revealed a heterogeneous tumor with uneven enhancement. Enlarged lymph nodes were noted in the lesser curvature of the stomach. On magnetic resonance imaging (MRI), T1- and T2-weighted imaging revealed hyperintensity in and hypointensity in the tumor, respectively, both of which increased substantially after uneven enhancement. Near total gastrectomy was performed, and the tumor was pathologically confirmed to be a gastric melanoma. Because no other possible primary site of malignant melanoma was suspected, a clinical diagnosis of PGM was made. The patient was followed for nearly 5 years, during which she received CT reexamination, but no recurrence or metastasis was observed. Certain imaging characteristics could be revealed in PGM. Imaging examination can be of great value in preoperative diagnosis, differential diagnosis, and follow-up of patients with PGM.