Prediction of Post-COVID-19 Pulmonary Fibrosis: An Integrated Approach Combining CT Radiomics and Clinical Characteristics.
To develop and validate a predictive model for post-COVID-19 pulmonary fibrosis (PCPF) by integrating CT radiomics features with clinical characteristics to facilitate early identification and intervention.
An observational study. Place and Duration of the Study: Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China, from December 2022 to January 2023.
This study enrolled 223 patients with COVID-19. Chest CT images and clinical data of the participants during their hospitalisation were collected. Follow-up chest CT scans were performed 3-12 months post-discharge to assess for PCPF. Participants were randomly divided into a training set (n = 156) and a testing set (n = 67). Using the least absolute shrinkage and selection operator (LASSO) regression, six optimised radiomic features were identified, and radiomic scores were calculated (Rad-scores). Univariate and multivariate logistic regression analyses screened clinical features, identifying age, lesion location, length of hospital stay, and lactate dehydrogenase (LDH) as independent predictors. Subsequently, a radiomic model, a clinical model, and a combined nomogram model incorporating Rad-scores and clinical predictors were constructed.
The combined nomogram demonstrated superior prediction. In the testing set, the areas under the curve (AUC) were 0.833, outperforming the clinical (AUC = 0.687) and radiomic (AUC = 0.811) models. The DeLong test confirmed that the nomogram significantly outperformed the clinical model (p < 0.05). Calibration and decision curve analyses verified the nomogram's excellent fit and provided substantial clinical net benefit.
The nomogram model based on CT radiomics and clinical features is promising for predicting PCPF.
Computed tomography, COVID-19, Post COVID-19 pulmonary fibrosis, Radiomics, Nomogram.
An observational study. Place and Duration of the Study: Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China, from December 2022 to January 2023.
This study enrolled 223 patients with COVID-19. Chest CT images and clinical data of the participants during their hospitalisation were collected. Follow-up chest CT scans were performed 3-12 months post-discharge to assess for PCPF. Participants were randomly divided into a training set (n = 156) and a testing set (n = 67). Using the least absolute shrinkage and selection operator (LASSO) regression, six optimised radiomic features were identified, and radiomic scores were calculated (Rad-scores). Univariate and multivariate logistic regression analyses screened clinical features, identifying age, lesion location, length of hospital stay, and lactate dehydrogenase (LDH) as independent predictors. Subsequently, a radiomic model, a clinical model, and a combined nomogram model incorporating Rad-scores and clinical predictors were constructed.
The combined nomogram demonstrated superior prediction. In the testing set, the areas under the curve (AUC) were 0.833, outperforming the clinical (AUC = 0.687) and radiomic (AUC = 0.811) models. The DeLong test confirmed that the nomogram significantly outperformed the clinical model (p < 0.05). Calibration and decision curve analyses verified the nomogram's excellent fit and provided substantial clinical net benefit.
The nomogram model based on CT radiomics and clinical features is promising for predicting PCPF.
Computed tomography, COVID-19, Post COVID-19 pulmonary fibrosis, Radiomics, Nomogram.