Prediction of Post-COVID-19 Pulmonary Fibrosis: An Integrated Approach Combining CT Radiomics and Clinical Characteristics.

OBJECTIVE: 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. STUDY DESIGN: 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. METHODOLOGY: 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. RESULTS: 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. CONCLUSION: The nomogram model based on CT radiomics and clinical features is promising for predicting PCPF. KEY WORDS: Computed tomography, COVID-19, Post COVID-19 pulmonary fibrosis, Radiomics, Nomogram.