Development and validation of a nomogram based on conventional and contrast-enhanced ultrasound for differentiating malignant from benign thyroid nodules.

Abstract

Background: Conventional ultrasound (US) has been routinely used for differential diagnosis of thyroid nodules, but its discriminatory performance remains unsatisfactory. This study aimed to develop and validate a prediction nomogram model based on conventional US and contrast-enhanced ultrasound (CEUS) features for differentiating malignant from benign thyroid nodules.

Methods: A total of 815 thyroid nodules with surgical pathology results and complete conventional US and CEUS data were retrospectively collected from the First People's Hospital of Qinzhou between January 2019 and July 2023. The nodules were grouped into a training cohort (n=571) and a validation cohort (n=244) at a 7:3 ratio. Independent risk factors of malignancy were selected by stepwise multivariate logistic regression analysis, and a prediction nomogram model was subsequently constructed. The diagnostic performance of the model was evaluated by the area under the receiver operating characteristic curve (AUC) in both the training and validation cohorts. The unnecessary fine-needle aspiration biopsy (FNAB) rate was calculated.

Results: Multivariate logistic regression analysis identified irregular margin, aspect ratio >1, and microcalcification from conventional US images, as well as hypo-enhancement intensity and ring enhancement from CEUS images, as independent predictors for malignancy. The AUC, sensitivity, specificity, and accuracy of the prediction nomogram model were 0.947 [95% confidence interval (CI): 0.928-0.966], 90.4%, 88.8%, and 89.8% in the training cohort, and 0.957 (95% CI: 0.928-0.986), 94.5%, 86.4%, and 91.8% in the validation cohort, respectively. Using the prediction model, the unnecessary FNAB rates reduced from 29.6% to 6.1% in the training cohort and from 29.3% to 6.7% in the validation cohort compared to the Chinese Thyroid Imaging Reporting and Data System. Decision curve analysis demonstrated good clinical utility of the nomogram model.

Conclusions: The prediction nomogram model incorporating conventional US and CEUS features could effectively distinguish between malignant and benign thyroid nodules and reduce unnecessary FNAB rates.