ArticleClinical rheumatology2024
Dual-energy computed tomography-based radiomics for differentiating patients with and without gout flares.
Article in Clinical rheumatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Development and Validation of a Machine Learning-Based Nomogram for Predicting Severe Pneumocystis jirovecii Pneumonia in Kidney Transplant Recipients.Annals of transplantation · 2026Article
- Status, challenges, and prospects of artificial intelligence application in gout diagnosis and treatment, drug research and development, and disease monitoring.Frontiers in medicine · 2026Review
- The Progress of Gout Prediction Models Based on Multi-source Data.Current rheumatology reviews · 2026Review
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Authors and funding
13 authors.
Funding
Abstract
backgroundThere is a current lack of data pertaining to the potential link between gout flares and dual-energy computed tomography radiomic features. This study aimed to construct and validate a comprehensive dual-energy computed tomography-based radiomics model for differentiating patients with and without gout flares.
methodsThe analysis included 200 patients, of whom 150 were confirmed to have experienced at least one flare in the past 12 months; the remaining 50 patients did not experience flares. The radiomic features of the tophi at the bilateral first metatarsophalangeal joints were extracted and analyzed. Optimal radiomic features were selected using the least absolute shrinkage and selection operator method, and logistic regression analysis was used to screen clinical characteristics and establish a clinical model. The optimal radiomic features were then combined with the identified independent clinical variables to develop a comprehensive model. The performances of the radiomic, clinical, and comprehensive models were evaluated using receiver operating characteristic curve analysis, calibration curves, and decision curve analysis.
resultsFour radiomic features distinguished patients with at least one flare from those without flares and were used to establish the radiomic model. Disease duration and hypertension were independent factors that differentiated flare occurrences. The radiomic, clinical, and comprehensive models showed favorable discrimination, with areas under the receiver operating characteristic curves of 0.76 (95% CI, 0.69-0.83), 0.72(95% CI, 0.63-0.80), and 0.79(95% CI, 0.73-0.86), respectively. The calibration curves (P > 0.05) showed that the differentiated values of the comprehensive model agreed well with the actual values. Decision curve analysis demonstrated that the comprehensive model achieved higher net clinical benefits than the use of either the radiomic or clinical model alone.
conclusionThe results of this study suggest that a radiomics model can distinguish patients with and without gout flares. Our proposed clinical radiomics nomogram can increase the efficacy of differentiating flare occurrence, which may facilitate the clinical decision-making process.
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