| Autor: |
Nie Han, Zhinan Guo, Diru Zhu, Yu Zhang, Yayi Qin, Guanheng Li, Xiaoli Gu, Lin Jin |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Immunology, Vol 15 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1664-3224 |
| DOI: |
10.3389/fimmu.2024.1417156 |
| Popis: |
ObjectivesQuantitatively 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.MethodsChest 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.ResultsA 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.ConclusionThe CT-based radiomics nomogram model achieved favorable efficacy in predicting low-risk RA-ILD patients. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
