Objective: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. Methods: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. Results: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462–2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). Conclusion: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

中文翻译：

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深度学习放射组学模型可识别患有潜在健康状况的 COVID-19 患者的不良结果：一项多中心研究


目的：2019 年冠状病毒病 (COVID-19) 已导致相当高的发病率和死亡率，特别是对于有基础健康状况的患者。迫切需要一种精确的预后工具来识别此类病例的不良结果。方法：从 4 个中心回顾性招募了 400 名有基础健康状况的 COVID-19 患者，其中包括 54 例死亡病例（标记为预后不良）和 346 例自初次 CT 扫描后出院或住院至少 7 天的患者。患者被分配到训练集 (n = 271)、测试集 (n = 68) 和外部测试集 (n = 61)。我们提出了一种最初的 CT 衍生混合模型，通过结合基于 3D-ResNet10 的深度学习模型和定量 3D 放射组学模型来预测 COVID-19 患者达到不良结果的概率。通过受试者工作特征曲线下面积（AUC）、生存分析和亚组分析来评估模型性能。结果：混合模型在测试和外部测试集上的 AUC 分别为 0.876（95% 置信区间：0.752-0.999）和 0.864（0.766-0.962），优于其他模型。生存分析验证了混合模型是死亡率的一个重要危险因素（风险比，2.049 [1.462–2.871]，P < 0.001），可以很好地将患者分为高风险和低风险，从而达到不良结果（P % 3C 0.001）。结论：结合深度学习和放射组学的混合模型可以通过初始 CT 扫描准确识别具有潜在健康状况的 COVID-19 患者的不良预后。强大的风险分层能力可以帮助预警死亡风险并制定及时的监测计划。