Ke Zan, Li Liang, Wang Li, Liu Huan, Lu Xuefang, Zeng Feifei, Zha Yunfei
Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, PR China.
Department of Infection Prevention and Control, Renmin Hospital of Wuhan University, Wuhan, PR China.
Acta Radiol. 2022 Mar;63(3):319-327. doi: 10.1177/0284185121994695. Epub 2021 Feb 18.
In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world.
To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia.
The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed.
Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867-0.957) in the training dataset and 0.907 (95% CI 0.849-0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts.
The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses.
2019年12月，一种名为2019冠状病毒病（COVID-19）的罕见呼吸道疾病爆发，引起了全球的高度关注。
开发并验证一种用于预测COVID-19肺炎患者死亡结局的放射组学列线图。
本研究包括一个训练数据集（n = 66）和一个验证数据集（n = 30），数据来自2020年1月至2020年3月的COVID-19患者。使用最小绝对收缩和选择算子（LASSO）Cox回归模型生成放射组学特征。从训练队列中得出放射组学评分（Rad-score）。构建放射组学模型、临床模型和整合模型，以评估放射组学特征/临床特征与COVID-19病例死亡率之间的关联。将放射组学特征与Rad-score以及独立临床因素相结合，构建放射组学列线图。
最终选择了7个与COVID-19死亡率相关的稳定放射组学特征。放射组学列线图基于一个由放射组学特征和临床风险因素组成的联合模型，该模型对COVID-19患者的死亡结局显示出最佳预测性能，在训练数据集中C指数为0.912（95%置信区间[CI] 0.867 - 0.957），在验证数据集中为0.907（95% CI 0.849 - 0.966）。校准曲线表明在训练和验证队列中预测与观察之间具有最佳一致性。
基于CT的放射组学列线图对COVID-19患者的总体生存风险显示出良好的预测效能，这有助于临床医生对高危患者进行重点随访并及时做出诊断。
