You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia. Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram. Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859-0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753-1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.
["Diagnosis"]