Wang Xiao-Hui, Xu Xiaopan, Ao Zhi, Duan Jun, Han Xiaoli, Tang Xing, Fu Yu-Fei, Wu Xu-Sha, Wang Xue, Zhu Linxiao, Zeng Wenbing, Guo Shuliang
Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
School of Biomedical Engineering, Air Force Medical University, Xi'an, China.
Front Med (Lausanne). 2021 Sep 16;8:730441. doi: 10.3389/fmed.2021.730441. eCollection 2021.
A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols. Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12-14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's -test and support vector machine-based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development. After Student's -test, 62 radiomics features showed significant inter-group differences ( < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case. The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.
相当一部分新冠病毒疾病（COVID-19）患者在出院后进行严重急性呼吸综合征冠状病毒2（SARS-CoV-2）逆转录聚合酶链反应（RT-PCR）检测时被发现再次呈阳性。对COVID-19复阳病例进行早期预测对于确定隔离期和制定临床方案至关重要。2020年2月10日至2020年3月3日期间，从中国重庆万州三峡中心医院出院的91例患者在12 - 14天内接受了鼻咽拭子SARS-CoV-2检测，最终纳入了50例数据完整的合格患者（32例男性和18例女性）。平均年龄为48±11.5岁。所有患者入院时均接受了非增强胸部CT检查。从CT图像中提取了总共568个放射组学特征，并根据病历收集了17个临床因素。采用学生t检验和基于支持向量机的递归特征消除（SVM-RFE）方法来确定用于判别模型开发的最佳特征子集。经过学生t检验，62个放射组学特征在复阳和未复阳病例之间显示出显著的组间差异（P<0.05），而临床特征均未显示出显著差异。这些显著特征通过SVM-RFE算法进一步筛选，最终确定了一个仅包含两个放射组学特征的更精简特征子集，在识别复阳病例时，其准确率和曲线下面积分别达到72.6%和0.773，实现了最佳预测性能。所提出的放射组学方法初步显示出在识别出院后康复的COVID-19患者中的复阳病例方面的潜力。需要将更多策略整合到当前流程中以提高其精度，并且需要一个包含多中心临床入组的更大数据库来广泛验证其性能。
