Diagnostic comparison of TI-RADS and a nomogram for thyroid nodules in Northwestern China

The aims of this study were: ① to evaluate the diagnostic efficacy of six mainstream TI-RADS (Thyroid Imaging Reporting and Data System) classification systems (C-TIRADS, ACR-TIRADS, etc.) in the Northwestern Chinese population; and ② to identify risk factors for malignant thyroid nodules (TNs) using logistic regression based on clinical and ultrasound features, construct a quantifiable scoring Nomogram model, enable rapid and objective risk assessment, and assist in clinical decision-making.

A total of 2,047 patients with TNs (1,433 malignant and 614 benign) were enrolled from January 2018 to January 2024 at Shaanxi Provincial People's Hospital. The nodules were divided into a training group (1,435 nodules) and a validation group (612 nodules) in a 7:3 ratio. Twelve characteristics were collected, including age, nodule size, margin, calcification, and the presence of suspicious lymph nodes. Independent risk factors were identified through univariate and multivariate logistic regression analyses to construct a Nomogram model. The model's performance was evaluated using receiver operating characteristic (ROC) curves, accuracy, and other metrics, and compared with the six traditional TI-RADS systems.

Ten independent risk factors were identified, including age, nodule size, and irregular margins. In the validation group, the Nomogram model achieved an accuracy of 78.4%, a sensitivity of 81.6%, a specificity of 71.7%, and an area under the ROC curve (AUC) of 0.849. The sensitivities of the six TI-RADS systems (C-TIRADS, ACR-TIRADS, EU-TIRADS, ATA Guidelines, Kwak-TIRADS, and AACE) for distinguishing benign and malignant nodules were 86.0%, 93.2%, 96.9%, 98.3%, 84.4%, and 98.1%, respectively; specificities were 55.6%, 34.8%, 25.3%, 22.2%, 57.1%, and 21.7%, respectively; accuracies were 76.1%, 74.3%, 73.7%, 73.7%, 75.8%, and 73.4%, respectively; and AUCs were 0.752, 0.661, 0.628, 0.617, 0.757, and 0.616, respectively, with no statistically significant differences among them. The Nomogram model significantly outperformed the traditional systems in measures such as AUC, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), Positive Likelihood Ratio (PLR), and Negative Likelihood Ratio (NLR) (P < 0.001).

The six traditional TI-RADS systems demonstrate similar but overall limited diagnostic efficacy in the Northwestern Chinese population. The Nomogram model, by integrating multidimensional features and applying a quantitative scoring approach, improves the accuracy and objectivity of malignancy risk assessment. Compared to traditional models, it offers better clinical utility, supports optimized decision-making, and helps reduce unnecessary invasive procedures.