Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics
10.13471/j.cnki.j.sun.yat-sen.univ（med.sci）.2023.0213
- VernacularTitle:基于CT影像特征预测COVID-19患者肺部病变进展
- Author:
Zhu-ping SU
1
;
Hai-bao WANG
1
;
Si-wei WANG
2
;
Ruo-mei LI
3
;
Qing-hong CHENG
3
;
Zong-gen GAO
4
;
Dong-feng LIU
4
;
Meng-qi ZHANG
5
Author Information
1. Department of Radiology，the First Affiliated hospital of Anhui Medical University， Hefei 230022， China
2. Hefei Ion Medical Center， the First Affiliated Hospital of USTC， Division of Life Sciences and Medicine， University of Science and Technology of China， Hefei 230093， China
3. Radiology department， The Second People’s Hospital of Hefei， Hefei 230011， China
4. Department of Infectious Disease， The First Affiliated Hospital of USTC， Division of Life Sciences and Medicine， University of Science and Technology of China， Hefei 230001， China
5. CT department， Hefei Binhu Hospital， The First People’s Hospital of Hefei， Hefei 230092， China
- Publication Type:Journal Article
- Keywords:
COVID-19;
computed tomography;
disease progression;
nomogram
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2023;44(2):286-294
- CountryChina
- Language:Chinese
-
Abstract:
ObjectivesBased 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. MethodsThis 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. ResultsThe 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. ConclusionThe 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%.
