Personalized assessment and treatment of severe patients with COVID-19 pneumonia have greatly affected the prognosis and survival of these patients. This study aimed to develop the radiomics models as the potential biomarkers to estimate the overall survival (OS) for the COVID-19 severe patients. A total of 74 COVID-19 severe patients were enrolled in this study, and 30 of them died during the follow-up period. First, the clinical risk factors of the patients were analyzed. Then, two radiomics signatures were constructed based on two segmented volumes of interest of whole lung area and lesion area. Two combination models were built depend on whether the clinic risk factors were used and/or whether two radiomics signatures were combined. Kaplan–Meier analysis were performed for validating two radiomics signatures and C-index was used to evaluated the predictive performance of all radiomics signatures and combination models. Finally, a radiomics nomogram combining radiomics signatures with clinical risk factors was developed for predicting personalized OS, and then assessed with respect to the calibration curve. Three clinical risk factors were found, included age, malignancy and highest temperature that influence OS. Both two radiomics signatures could effectively stratify the risk of OS in COVID-19 severe patients. The predictive performance of the combination model with two radiomics signatures was better than that only one radiomics signature was used, and became better when three clinical risk factors were interpolated. Calibration curves showed good agreement in both 15 d survival and 30 d survival between the estimation with the constructed nomogram and actual observation. Both two constructed radiomics signatures can act as the potential biomarkers for risk stratification of OS in COVID-19 severe patients. The radiomics+clinical nomogram generated might serve as a potential tool to guide personalized treatment and care for these patients.

对重症 COVID-19 肺炎患者的个性化评估和治疗极大地影响了这些患者的预后和生存。本研究旨在开发放射组学模型作为潜在的生物标志物，以估计 COVID-19 重症患者的总生存期 (OS)。本研究共招募了 74 名 COVID-19 重症患者，其中 30 人在随访期间死亡。首先对患者的临床危险因素进行分析。然后，基于整个肺区域和病变区域的两个分割感兴趣体积构建两个放射组学特征。根据是否使用临床风险因素和/或是否结合了两个放射组学特征，建立了两个组合模型。Kaplan-Meier 分析用于验证两个放射组学特征，C-index 用于评估所有放射组学特征和组合模型的预测性能。最后，开发了一种将放射组学特征与临床风险因素相结合的放射组学列线图，用于预测个性化 OS，然后根据校准曲线进行评估。发现了三个临床危险因素，包括影响 OS 的年龄、恶性肿瘤和最高温度。这两个放射组学特征都可以有效地对 COVID-19 重症患者的 OS 风险进行分层。具有两个放射组学特征的组合模型的预测性能优于仅使用一个放射组学特征的预测性能，并且在插入三个临床危险因素时变得更好。校准曲线显示，在构建列线图的估计与实际观察之间，15 d 生存期和 30 d 生存期均具有良好的一致性。这两个构建的放射组学特征都可以作为 COVID-19 重症患者 OS 风险分层的潜在生物标志物。生成的放射组学+临床列线图可能作为指导这些患者个性化治疗和护理的潜在工具。