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

Authors :
Songnan Qin
Bingxuan Jiao
Bing Kang
Haiou Li
Hongwu Liu
Congshan Ji
Shifeng Yang
Hongtao Yuan
Ximing Wang
Source :
Frontiers in Immunology, Vol 14 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

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.

Details

Language :
English
ISSN :
16643224
Volume :
14
Database :
Directory of Open Access Journals
Journal :
Frontiers in Immunology
Publication Type :
Academic Journal
Accession number :
edsdoj.182fd4b8a95647268ac5d3ece893627d
Document Type :
article
Full Text :
https://doi.org/10.3389/fimmu.2023.1213008