Incorporation of CEUS and SWE parameters into a multivariate logistic regression model for the differential diagnosis of benign and malignant TI-RADS 4 thyroid nodules

• Published in: Endocrine Vol. 83; no. 3; pp. 691 - 699
• Main Authors: Li, Hong-Jing, Sui, Guo-Qing, Teng, Deng-Ke, Lin, Yuan-Qiang, Wang, Hui
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Diabetes; Diagnosis, Differential; Differential diagnosis; Elasticity Imaging Techniques; Endocrinology; Humanities and Social Sciences; Humans; Internal Medicine; Logistic Models; Medicine; Medicine & Public Health; multidisciplinary; Nodules; Original; Original Article; Regression analysis; Retrospective Studies; Science; Thyroid; Thyroid gland; Thyroid Nodule - diagnostic imaging; Thyroid Nodule - pathology; Ultrasonography; Thyroid gland; Time–intensity curve (TIC); ACR TI-RADS; Ultrasonography
• ISSN: 1559-0100; 1355-008X; 1559-0100
• DOI: 10.1007/s12020-023-03524-2

Purpose To investigate the diagnostic value of contrast-enhanced ultrasound (CEUS) quantitative analysis parameters combined with shear wave elastography (SWE) quantitative parameters in the differentiation of benign and malignant ACR TI-RADS category 4 thyroid nodules and to provide a more effective reference for clinical work. Methods We analyzed 187 category 4 nodules, including 132 nodules in the development cohort and 55 nodules in the validation cohort, divided the development cohort into benign and malignant groups, and analyzed the differences in all CEUS and SWE quantitative parameters between the two groups. We selected the highest AUC of the two parameters, performed binary logistic regression analysis with the ACR TI-RADS score and constructed a diagnostic model. ROC curves were applied to evaluate their diagnostic efficacy. Results 1) The diagnostic model had an AUC of 0.926, sensitivity of 87.5%, specificity of 86.8%, diagnostic threshold of 3, accuracy of 87.12%, positive predictive value of 86.15%, and negative predictive value of 88.06%. 2) The diagnostic model had an AUC of 0.890 in the validation cohort, sensitivity of 81.5%, specificity of 79.6%, and accuracy of 80.00%. Conclusion The combined multiparameter construction of the nodule diagnostic model can effectively improve the diagnostic efficacy of 4 types of thyroid nodules and provide a new reference index for clinical diagnostic work.