Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease - Qin, Songnan; Jiao, Bingxuan; Kang, Bing; Li, Haiou; Liu, Hongwu; Ji, Congshan; Yang, Shifeng; Yuan, Hongtao; Wang, Ximing (Frontiers in Immunology, 2023)
It is significant to assess severity and predicting the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical-factor model was built by demographics and CT features and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses.The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.total of 245 patients from two clinical centers (dataset 1, n = 202 and dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis and 9 radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort [AUC, 0.887 (95% CI: 0.827, 0.940)], the internal-validation cohort [AUC, 0.885 (95% CI: 0.816, 0.922)] and the external-validation cohort [AUC,0.85 (95% CI: 0.720, 0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical-factor model and radiomics signature in terms of clinical usefulness.The CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.