Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19
Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19
复制标题
计算机断层扫描的放射组学分析有助于预测 COVID-19 的不良预后结果
DOI：
10.7150/thno.46428
复制
发表时间：
2020-01-01
期刊：
影响因子：
12.4
通讯作者：
Tian, Jie
中科院分区：
文献类型：
--
作者：
Wu, Qingxia;Wang, Shuo;Tian, Jie
理由：鉴于COVID-19的快速传播，更新的风险分层预后工具可以帮助临床医生识别预后较差的高危患者。我们的目标是通过胸部CT开发一种无创且易于使用的预后特征，以单独预测COVID-19患者的不良预后（死亡、需要机械通气或入住重症监护病房）。方法：回顾性收集2019年11月29日至2020年2月19日4个中心共492例COVID-19患者。由于从症状出现到第一次CT扫描的不同时间可能影响预后模型，我们将492例患者分为两组：1)早期组：在症状出现后一周内（0-6天，n = 317）进行CT扫描；2)晚期组：症状出现1周后（≥7天，n = 175）行CT扫描。在每组中，我们根据中心将患者分为主要队列（早期组n = 212，晚期组n = 139）和外部独立验证队列（早期组n = 105，晚期组n = 36）。我们在两组患者中建立了两个独立的放射组学模型。首先，提出了一种自动分割肺体积的方法，用于放射组学特征提取。其次，我们采用了几种图像预处理方法来提高放射组学特征的再现性：1)在体素重采样前应用低通高斯滤波器来防止混叠；2)进行ComBat以协调每个扫描仪的放射组学特征；3)通过旋转、平移、生长/收缩等图像变换测试放射组学特征的稳定性。第三，我们使用最小绝对收缩和选择算子（LASSO）构建放射组学特征（RadScore）。随后，我们进行了Fine-Gray竞争风险回归，以建立临床模型和临床放射组学特征（CrrScore）。最后，从两个方面评估三个预后指标（临床模型、RadScore和CrrScore）的表现：1)累积不良结局概率预测；2) 28天预后预测差。我们还进行了分层分析，以探讨CrrScore与不同年龄、类型和合并症亚组的不良预后之间的潜在关联。结果：早期组CrrScore在估计不良预后（C-index = 0.850）和预测28天不良预后概率（AUC = 0.862）方面表现最佳。在晚期组，单独使用RadScore预测不良预后（C-index = 0.885）和28天不良预后概率（AUC = 0.976）的效果与CrrScore相似。此外，两组的RadScore成功地将COVID-19患者分为低或高RadScore组，在训练组和验证组中生存时间差异显著（均P < 0.05）。两组的CrrScore还可以对联合队列中不同年龄、类型和合并症亚组的不同预后患者进行显著分层（均P < 0.05）。结论：本研究提出了一种基于CT成像的无创定量预测COVID-19患者预后不良的工具。考虑到医疗资源不足，我们的研究可能表明，胸部CT放射组学特征对预测晚期COVID-19患者的不良预后更有效和理想。对于早期患者，将放射组学特征与临床危险因素相结合，可以更准确地预测个体预后不良，从而实现对COVID-19的适当管理和监测。
Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19. Methods: From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (≥7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups. Results: In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low- or high-RadScore groups with significantly different survival time in the training and validation cohorts (all P < 0.05). The CrrScore in both groups can also significantly stratify patients with different prognoses regarding different age, type, and comorbidities subgroups in the combined cohorts (all P < 0.05). Conclusions: This research proposed a non-invasive and quantitative prognostic tool for predicting poor outcome in patients with COVID-19 based on CT imaging. Taking the insufficient medical recourse into account, our study might suggest that the chest CT radiomics signature of COVID-19 is more effective and ideal to predict poor outcome in the late-phase COVID-19 patients. For the early-phase patients, integrating radiomics signature with clinical risk factors can achieve a more accurate prediction of individual poor prognostic outcome, which enables appropriate management and surveillance of COVID-19.