To develop and validate an effective model for distinguishing COVID‐19 from bacterial pneumonia. In the training group and internal validation group, all patients were randomly divided into a training group (n = 245) and a validation group (n = 105). The whole lung lesion on chest computed tomography (CT) was drawn as the region of interest (ROI) for each patient. Both feature selection and model construction were first performed in the training set and then further tested in the validation set with the same thresholds. Additional tests were conducted on an external multicentre cohort with 105 subjects. The diagnostic model of LightGBM showed the best performance, achieving a sensitivity of 0.941, specificity of 0.981, accuracy of 0.962 on the validation dataset. In this study, we established a differential model to distinguish between COVID‐19 and bacterial pneumonia based on chest CT radiomics and clinical indexes.

中文翻译：

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使用胸部计算机断层扫描的放射线照相法和临床特征区分COVID-19和细菌性肺炎

开发并验证区分细菌性肺炎的有效COVID-19模型。在训练组和内部验证组中，将所有患者随机分为训练组（n = 245）和验证组（n = 105）。将胸部计算机断层扫描（CT）上的整个肺部病变绘制为每个患者的关注区域（ROI）。首先在训练集中执行特征选择和模型构建，然后在验证集中使用相同的阈值对其进行进一步测试。在105名受试者的外部多中心队列中进行了其他测试。在验证数据集上，LightGBM的诊断模型显示出最佳性能，灵敏度为0.941，特异性为0.981，准确性为0.962。在这个研究中，