基于 CT 影像组学特征的模型预测 2019 冠状病毒病（COVID-19）患者 RT-PCR 转为阴性。
A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients.
机构信息
Department of Emergency Medicine, The First Affiliated Hospital of China Medical University, Nanjing North Street 155, Shenyang, 110001, Liaoning Province, China.
Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, China.
出版信息
BMC Med Imaging. 2020 Oct 20;20(1):118. doi: 10.1186/s12880-020-00521-z.
BACKGROUND
Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment.
METHODS
From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.
RESULTS
The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.
CONCLUSION
The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.
背景
2019 年冠状病毒病（COVID-19）已成为全球大流行疾病。根据中国的诊断和治疗指南，阴性逆转录-聚合酶链反应（RT-PCR）是 COVID-19 患者出院的关键标准。然而，在恢复期，对 COVID-19 患者进行重复 RT-PCR 检测会导致医疗废物的产生和住院时间的延长。我们的目的是评估一种基于胸部计算机断层扫描（CT）放射组学特征和临床特征的模型，以预测临床治疗过程中 RT-PCR 结果的阴性。
方法
2020 年 2 月 10 日至 3 月 10 日，回顾性纳入方仓庇护医院 203 例轻症 COVID-19 患者（训练组：n=141；测试组：n=62），并收集临床特征。使用深度学习算法对胸部 CT 图像上的肺部异常进行分割。自动提取 CT 定量特征和放射组学特征。比较 RT-PCR 阴性组和 RT-PCR 阳性组之间的临床特征和 CT 定量特征。单变量逻辑回归和 Spearman 相关分析确定与 RT-PCR 阴性结果最强相关的特征，并建立多变量逻辑回归模型。对两个队列进行诊断性能评估。
结果
与 RT-PCR 阳性组相比，RT-PCR 阴性组从症状发作到 CT 检查的时间间隔更长（中位数 23 天比 16 天，p<0.001）。其他临床特征或 CT 定量特征无显著差异。除了从症状发作到 CT 检查的时间间隔外，还选择了 9 个 CT 放射组学特征用于该模型。ROC 曲线分析显示，训练组和测试组区分 RT-PCR 阴性组的 AUC 分别为 0.811 和 0.812，灵敏度/特异性分别为 0.765/0.625 和 0.784/0.600。
结论
结合 CT 放射组学特征和临床数据的模型有助于预测临床治疗过程中 RT-PCR 结果的阴性，提示适当的 RT-PCR 重复检测时间。
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