A nomogram model combining computed tomography-based radiomics and Krebs von den Lungen-6 for identifying low-risk rheumatoid arthritis-associated interstitial lung disease.

Objectives: Quantitatively assess the severity and predict the mortality of interstitial lung disease (ILD) associated with Rheumatoid arthritis (RA) was a challenge for clinicians. This study aimed to construct a radiomics nomogram based on chest computed tomography (CT) imaging by using the ILD-GAP (gender, age, and pulmonary physiology) index system for clinical management. Methods: Chest CT images of patients with RA-ILD were retrospectively analyzed and staged using the ILD-GAP index system. The balanced dataset was then divided into training and testing cohorts at a 7:3 ratio. A clinical factor model was created using demographic and serum analysis data, and a radiomics signature was developed from radiomics features extracted from the CT images. Combined with the radiomics signature and independent clinical factors, a nomogram model was established based on the Rad-score and clinical factors. The model capabilities were measured by operating characteristic curves, calibration curves and decision curves analyses. Results: A total of 177 patients were divided into two groups (Group I, n = 107; Group II, n = 63). Krebs von den Lungen-6, and nineteen radiomics features were used to build the nomogram, which showed favorable calibration and discrimination in the training cohort [AUC, 0.948 (95% CI: 0.910-0.986)] and the testing validation cohort [AUC, 0.923 (95% CI: 0.853-0.993)]. Decision curve analysis demonstrated that the nomogram performed well in terms of clinical usefulness. Conclusion: The CT-based radiomics nomogram model achieved favorable efficacy in predicting low-risk RA-ILD patients.