A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.
A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.
复制标题
基于深度学习的定量计算机断层扫描模型用于预测COVID-19的严重程度：对196名患者的回顾性研究
DOI：
10.21037/atm-20-2464
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发表时间：
2021-03
影响因子：
--
通讯作者：
Shan F
中科院分区：
文献类型：
--
作者：
Shi W;Peng X;Liu T;Cheng Z;Lu H;Yang S;Zhang J;Wang M;Gao Y;Shi Y;Zhang Z;Shan F
根据临床表现评估2019冠状病毒病（COVID-19）的严重程度，至今未能满足迫切的临床需要。我们的目标是建立一个基于定量计算机断层扫描（CT）和初始临床特征的深度学习（DL）模型，以预测COVID-19的严重程度。本中心于2020年1月20日至2月10日期间共纳入196例确诊COVID-19的住院患者，并分为重症和非重症组。回顾性收集两组患者入院时的临床影像学资料并进行比较。根据最小绝对收缩和选择算子（LASSO）Logistic回归分析确定最佳临床-放射学特征，并通过五重交叉验证建立预测诺模图模型。进行受试者工作特征（ROC）分析，比较诺模图模型的受试者工作特征曲线下面积（AUC）、单变量分析中有意义的定量CT参数和肺炎严重程度指数（PSI）。重度组（45例）PSI高于非重度组（151例）（P<0.001）。DL定量CT显示重症组感染体积（MOICT）和全肺感染百分比（POICT）均高于对照组（P均<0.001）。以MOICT和临床特征（包括年龄、分化抗原4（CD 4）+ T细胞计数、血清乳酸脱氢酶（LDH）和C反应蛋白（CRP））为基础建立列线图模型。模型、MOICT、POICT和PSI评分的AUC值分别为0.900、0.813、0.805和0.751。诺模图模型在预测严重程度方面明显优于其他三个参数（分别为P=0.003、P=0.001和P<0.001）。尽管定量CT参数和PSI可以很好地预测COVID-19的严重程度，但基于DL的定量CT模型更有效。
The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOICT and clinical features, including age, cluster of differentiation 4 (CD4)+ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOICT, POICT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively). Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.