Department of Radiology, Meizhou People's Hospital, Meizhou, 514031, Guangdong, People's Republic of China.
Department of Radiology, 2nd Affiliated Hospital, Shantou University Medical College, Shantou, 515000, Guangdong, People's Republic of China.
Eur Radiol. 2020 Sep;30(9):4893-4902. doi: 10.1007/s00330-020-06829-2. Epub 2020 Apr 16.
Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone.
A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram.
Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits.
Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.
• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.9661.000) and 0.936 (95% CI 0.8661.000) in the primary and validation cohorts, respectively.
在疫情期间，快速准确地诊断 2019 年冠状病毒病（COVID-19）至关重要。我们旨在确定患有 COVID-19 和非 COVID-19 肺炎患者的 CT 影像学和临床表现之间的差异，并开发和验证一种仅基于放射语义和临床特征的 COVID-19 诊断模型。
回顾性纳入来自五家机构的 70 例 COVID-19 和 66 例非 COVID-19 肺炎患者的连续队列。患者分为主要队列（n=98）和验证队列（n=38）。采用卡方检验、学生 t 检验和 Kruskal-Wallis H 检验比较两组 1745 个病变和 67 个特征。通过多元逻辑回归利用放射语义和临床特征构建三个模型。通过受试者工作特征曲线量化所开发模型的诊断效能。通过决策曲线分析和列线图评估临床应用。
确定了 18 个放射语义特征和 17 个临床特征存在显著差异。除了肺外周的磨玻璃影（p=0.032）和实变（p=0.001）外，病变大小（1-3cm）也对 COVID-19 的诊断具有显著意义（p=0.027）。肺评分无显著差异（p=0.417）。三个诊断模型的曲线下面积值高达 0.986（95%CI 0.966~1.000）。临床和放射语义模型提供了更好的诊断性能和更可观的净收益。
仅基于 CT 影像学和临床表现，就可以区分患有 COVID-19 和非 COVID-19 的肺炎患者。由放射语义和临床特征组成的模型在 COVID-19 的诊断中具有出色的性能。
