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Updated: Jul 20, 2026
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Ultrasound-Based Prediction Model for Distinguishing Malignant from Benign Thyroid Nodules with Peripheral
Song Bai1, Linghu Wu1, Youhuan Su1
1Department of Ultrasound, Shenzhen People's Hospital, Shenzhen 518020, Guangdong, China (S.B., L.W., Y.S., J.X., F.D.).
Rationale And Objectives:
The differential diagnosis of thyroid nodules with peripheral calcifications by ultrasound (US) has always been source of confusion. This study aimed to develop and validate a predictive US-based nomogram model to differentiate malignant from benign nodules with peripheral calcifications.
Methods:
Of the 8359 thyroid nodules scanned by ultrasonography between January 2017 and January 2025, 380 nodules with peripheral calcifications were included, with confirmed pathological results and US examinations. 268 nodules were included in the training cohort, and 112 nodules were included in the validation cohort. The candidate variables included age, gender, and the image features obtained from grayscale US. Independent risk factors for malignant thyroid nodules were determined by univariate and multivariate analyses, and a predictive nomogram model was developed. The performance of the US nomogram was assessed by the area under the curve (AUC), calibration curve, and decision curve analysis (DCA) results.
Results:
Univariate and multivariate logistic regression analyses revealed that the halo sign, extrusion beyond calcification, type of peripheral calcification, margin, internal echogenicity, and composition were significant independent predictors for malignant thyroid nodules with peripheral calcifications. The nomogram model based on the six variables exhibited excellent calibration and discrimination in the training and validation cohorts, with AUC values of 0.904 and 0.882, respectively. The DCA showed that a probability threshold of 0.11-0.83 could benefit patients clinically.
Conclusion:
The US-based nomogram model can potentially predict the malignant risk of thyroid nodules with peripheral calcifications, thereby helping to enhance diagnostic accuracy.
