Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China.
Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China.
Sci Rep. 2020 Dec 16;10(1):22083. doi: 10.1038/s41598-020-79097-1.
To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm versus 101.12 ± 127 cm, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm versus 6.63 ± 14.91 cm, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.
探讨人工智能（AI）辅助量化在初始胸部 CT 对 2019 年冠状病毒病（COVID-19）患者疾病进展和临床结局预测中的价值。回顾性纳入确诊 COVID-19 感染且初始为非重症型的患者。入院时的初始 CT 扫描用于影像学分析。肉眼评估磨玻璃密度（GGO）、实变和其他表现的存在。根据病变受累范围计算 CT 严重程度评分。此外，还进行了基于 AI 的 GGO 和实变体积定量。共纳入 123 例患者（平均年龄：64.43±14.02；62 名男性）。进展至重症组更常出现 GGO+实变，而非重症组更常出现单纯 GGO。与非重症组相比，进展至重症组患者的 GGO 体积更大（167.33±167.88 cm 比 101.12±127 cm，p=0.013），实变体积也更大（40.85±60.4 cm 比 6.63±14.91 cm，p<0.001）。在影像学参数中，实变体积在区分非重症与进展至重症组（AUC=0.796，p<0.001）和有无危重症患者（AUC=0.754，p<0.001）方面具有最大的曲线下面积（AUC）。多变量回归分析显示，实变体积和年龄是疾病进展的两个最强预测因素（危险比：1.053 和 1.071，p：0.006 和 0.008），而年龄和糖尿病是不良结局的预测因素。初始胸部 CT 上的实变体积是疾病严重程度进展的最强预测因素，较大的实变体积与不良临床结局相关。
