Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics

SU Zhu-ping; WANG Hai-bao; WANG Si-wei; LI Ruo-mei; CHENG Qing-hong; GAO Zong-gen; LIU Dong-feng; ZHANG Meng-qi

doi:10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0213

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Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics

Clinical Research | 更新时间：2024-02-19

- Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics
- Journal of Sun Yat-sen University(Medical Sciences) Vol. 44, Issue 2, Pages: 286-294(2023)
- 作者机构：
  
  1.安徽医科大学第一附属医院放射科，安徽合肥 230022
  2.中国科技大学附属第一医院离子医学中心（合肥离子医学中心）影像科，安徽合肥 230093
  3.合肥市第二人民医院新区放射科，安徽合肥 230011
  4.中国科学技术大学附属第一医院感染病区介入科，安徽合肥 230001
  5.合肥市第一人民医院滨湖院区CT室，安徽合肥 230092
- 作者简介：
  
  WANG Hai-bao， E-mail： wanghaibao916@163.com
  WANG Si-wei， E-mail： 372891850@qq.com
- 基金信息：
- DOI：10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0213
  CLC： R816
- Received：24 September 2022，
  
  Published：20 March 2023
- 稿件说明：
移动端阅览
苏祝平,王海宝,王嗣伟等.基于CT影像特征预测COVID-19患者肺部病变进展[J].中山大学学报（医学科学版）,2023,44(02):286-294.

SU Zhu-ping,WANG Hai-bao,WANG Si-wei,et al.Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(02):286-294.
苏祝平,王海宝,王嗣伟等.基于CT影像特征预测COVID-19患者肺部病变进展[J].中山大学学报（医学科学版）,2023,44(02):286-294. DOI： 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0213.

SU Zhu-ping,WANG Hai-bao,WANG Si-wei,et al.Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(02):286-294. DOI： 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0213.

摘要

目的

基于不同阶段COVID-19患者肺部病变变化，利用CT影像学特征建立列线图模型，探讨其预测病变是否进展的效能。

方法

对136例新冠肺炎患者进行回顾性研究，均经2次以上CT扫描。这些患者数据被分成三个队列（训练队列，以及验证队列1和2）。训练队列中的患者根据发热症状开始至首次CT的时间分为三组，分析比较各组之间临床表现和CT特征。根据患者的CT特征构建了一个预测疾病进展的列线图，并对其性能进行了评估。

结果

训练队列包括41名患者。根据三个CT特征：不规则条索影、充气支气管征和不规则形态病灶的比例≥50%，生成了预测疾病进展的列线图，AUC（95%CI）=0.906（0.817，0.995）。训练队列的C指数为0.906，内部验证的C指数为0.892。验证队列1（34例）：

AUC（95%CI）=0.889（0.793，0.984）；验证队列2（61例）： AUC（95%CI）=0.876（0.706，1.000）。校准曲线表明，列线图预测值与观测值具有较好的一致性。

结论

基于CT影像组学建立的列线图模型可以预测患者肺部病灶的转归，具有较高的敏感性和特异性。根据新冠肺炎患者的CT影像特征变化，即当出现不规则条索影、充气支气管征和不规则形态病灶的比例≥50%时，肺部病灶将会得到改善。

Abstract

Objectives

Based on the changes of lung lesions in patients with COVID-19 at different stages， a nomogram model describing CT image features was established by radiomics method to explore its efficacy in predicting the progression of the disease.

Methods

This retrospective study enrolled 136 patients with COVID-19 pneumonia who received at least two CTs including three cohorts （training cohort and validation cohort 1 and 2）. Patients in the training cohort were divided into three groups according to time between onset of fever symptoms and the first CT. The clinical manifestations and CT features of each group were analyzed and compared. A nomogram to predict disease progression was constructed according to the CT features of the patients， and its performance was evaluated.

Results

The training cohort consisted of 41 patients.A nomogram was generated to predict disease progression based on three CT features： irregular strip shadow， air bronchial sign， and the proportion of lesions with irregular shape ≥50%.

AUC（95%CI）=0.906（0.817，0.995）.The C index of the training cohort was 0.906， and the C index of the internal verification was 0.892. AUC（95%CI）of the validation cohort 1 （34 cases） =0.889（0.793，0.984）；AUC（95%CI）of the validation cohort 2 （61 cases）=0.876（0.706，1.000）.The calibration curves show that the predicted values of the nomogram are in good agreement with the observed values.

Conclusion

The nomogram model based on CT radiomics can predict the outcome of lung lesions in patients with high sensitivity and specificity.According to the changes of CT image characteristics of patients with COVID-19， lung lesions will be improved when the proportion of irregular cable shadow， air bronchogram and irregular lesions is greater than 50%.

关键词

Keywords

references

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