Optimized dual-source dual-energy computed tomography nomogram model integrating background normalization improves solid thyroid nodules diagnosis

Background: Accurate preoperative diagnosis of benign and malignant thyroid nodules is crucial for personalized patient treatment and management. This study aimed to create a dual-source dual-energy computed tomography (DS-DECT) based nomogram model to predict the risk of malignant thyroid nodules. Methods: A total of 263 patients (288 nodules) with thyroid nodules who underwent preoperative neck DS-DECT scans and were pathologically confirmed were included in this study. The computed tomography (CT) radiological features and DS-DECT-derived quantitative parameters of the nodules were collected. Subsequently, the thyroid nodules were randomly partitioned into a training cohort (n=201) and a validation cohort (n=87) at a ratio of 7:3. Univariate logistic regression analysis identified predictors (P