Development and Validation of an Ultrasound-Based Clinical Radiomics Nomogram for Diagnosing Gouty Arthritis

This study aimed to develop and validate a diagnostic model for gouty arthritis by integrating ultrasonographic radiomic features with clinical parameters. A total of 604 patients suspected of having gouty arthritis were enrolled and randomly divided into a training set (n=483) and a validation set (n=121) in a 4:1 ratio. Univariate and multivariate analyses were conducted on the clinical data to identify statistically significant clinical features for constructing an initial diagnostic model. Key radiomic features were identified in the training set using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis to establish a radiomic model. A composite clinical-radiomic nomogram was then developed by combining clinical features (such as CRP, ESR and uric acid level) and radiomic features through logistic regression. The predictive performance of the clinical model, radiomic model, and clinical-radiomic nomogram was evaluated in the validation set using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The clinical-radiomic nomogram, which integrated imaging features and clinical characteristics via logistic regression, demonstrated superior predictive performance in the validation set, with an AUC of 0.936 (95% CI: 0.885-0.986), surpassing both the clinical model (AUC=0.924, 95%CI:0.873-0.976) and radiomic model (AUC=0.828, 95%CI: 0.738-0.918) alone. DCA further confirmed the clinical utility of this model, particularly in differentiating between gouty and non-gouty arthritis. Compared to standalone clinical or radiomic models, the ultrasonography-based clinical-radiomic model exhibited enhanced predictive accuracy for diagnosing gouty arthritis, presenting a novel and promising approach for the early diagnosis and management of gouty arthritis.