The worldwide spread of coronavirus disease (COVID-19) has become a threat to global public health. It is of great importance to rapidly and accurately screen and distinguish patients with COVID-19 from those with community-acquired pneumonia (CAP). In this study, a total of 1,658 patients with COVID-19 and 1,027 CAP patients underwent thin-section CT and were enrolled. All images were preprocessed to obtain the segmentations of infections and lung fields. A set of handcrafted location-specific features was proposed to best capture the COVID-19 distribution pattern, in comparison to the conventional CT severity score (CT-SS) and radiomics features. An infection size-aware random forest method (iSARF) was proposed for discriminating COVID-19 from CAP. Experimental results show that the proposed method yielded its best performance when using the handcrafted features, with a sensitivity of 90.7%, a specificity of 87.2%, and an accuracy of 89.4% over state-of-the-art classifiers. Additional tests on 734 subjects, with thick slice images, demonstrates great generalizability. It is anticipated that our proposed framework could assist clinical decision making.

冠状病毒病 (COVID-19) 的全球传播已成为对全球公共卫生的威胁。快速准确地筛查和区分 COVID-19 患者与社区获得性肺炎 (CAP) 患者非常重要。在这项研究中，共有 1,658 名 COVID-19 患者和 1,027 名 CAP 患者接受了薄层 CT 检查并被纳入。所有图像都经过预处理以获得感染和肺野的分割。与传统的 CT 严重程度评分 (CT-SS) 和放射组学特征相比，提出了一组手工制作的特定于位置的特征，以最好地捕捉 COVID-19 分布模式。提出了一种感染大小感知随机森林方法 (iSARF)，用于区分 COVID-19 和 CAP。实验结果表明，与最先进的分类器相比，所提出的方法在使用手工特征时产生了最佳性能，灵敏度为 90.7%，特异性为 87.2%，准确度为 89.4%。使用厚切片图像对 734 名受试者进行的额外测试证明了其具有很强的普遍性。预计我们提出的框架可以帮助临床决策。