Predictive Value of the Nomogram Model Based on Multimodal Ultrasound Features for Benign and Malignant Thyroid Nodules of C-TIRADS Category 4

Abstract

To explore the predictive value of the nomogram model based on multimodal ultrasound features for benign and malignant thyroid nodules of C-TIRADS category 4. A retrospective analysis was conducted on the general conditions and ultrasound features of patients who underwent thyroid ultrasound examination and fine needle aspiration biopsy (FNA) or thyroidectomy at the Affiliated Hospital of Zunyi Medical University from April 2020 to April 2023. Predictive signs for benign and malignant nodules of thyroid C-TIRADS category 4 were screened through LASSO regression and multivariate logistic regression analysis to construct a nomogram prediction model. The predictive efficiency and accuracy of the model were assessed through ROC curves and calibration curves. Seven independent risk factors in the predictive model for benign and malignant thyroid nodules of C-TIRADS category 4 were growth pattern, morphology, microcalcifications, SR, arterial phase enhancement intensity, initial perfusion time, and PE [%]. Based on these features, the area under the curve (AUC) of the constructed prediction model was 0.971 (p ＜ .001, 95% CI: 0.952–0.989), with a prediction accuracy of 93.1%. Internal validation showed that the nomogram calibration curve was consistent with reality, and the decision curve analysis indicated that the model has high clinical application value. The nomogram prediction model constructed based on the multimodal ultrasound features of thyroid nodules of C-TIRADS category 4 has high clinical application value.