Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease

• Published in: Frontiers in immunology 2023-10, Vol.14, p.1213008-1213008
• Main Authors: Qin, Songnan, Jiao, Bingxuan, Kang, Bing, Li, Haiou, Liu, Hongwu, Ji, Congshan, Yang, Shifeng, Yuan, Hongtao, Wang, Ximing
• Format: Article
• Language: English
• Subjects: computed tomography; connective tissue diseases; Immunology; interstitial lung diseases; machine learning; radiomics
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2023.1213008

Rationale and introductionIt is of significance to assess the severity and predict 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.Materials and methodsPatients 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.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (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.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.