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Updated: Aug 18, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Predicting malignancy in thyroid nodules based on conventional ultrasound and elastography: the value of predictive
Ying Zhang1,2,3,4, Qiong-Yi Huang5, Chang-Jun Wu6
1Center of Minimally Invasive Treatment for Tumor, Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Background:
This study aimed to establish predictive models based on features of Conventional Ultrasound (CUS) and elastography in a multi-center study to determine appropriate preoperative diagnosis of malignancy in thyroid nodules with different risk stratification based on 2017 Thyroid Imaging Reporting and Data System by the American College of Radiology (ACR TI-RADS) guidelines.
Methods:
Five hundred forty-eight thyroid nodules from three centers pathologically confirmed by the cytology or histology were retrospectively enrolled in the study, which were examined by CUS and elastography before fine needle aspiration (FNA) and surgery. Characteristics of CUS of thyroid nodules were reviewed according to 2017 ACR TI-RADS. Binary logistic regression analysis was used to develop the prediction models based on the different risk stratification of CUS features and elastography which were statistically significant. Values of predictive models were evaluated regarding the discrimination and calibration.
Results:
Binary logistic regression showed that patients' age, taller-than-wider, lobulated or irregular boundary, extra-thyroid extension, microcalcification and the elastic parameter of Virtual touch tissue imaging quantification (VTIQ) max were independent predictors for thyroid malignancy (p < 0.05) in the ACR model and showed the area under the curve (AUC) in training (0.912) and validation cohort (internal and external: 0.877 vs 0.935). Predictive models showed predictors in ACR TR4 and TR5 for malignancy and diagnostic performance of AUC in training, internal and external validation cohort respectively: the VTIQ max (p < 0.001) with AUC of 0.809 vs 0.842 vs 0.705 and the age, taller than wide, VTIQ max variables with AUC of 0.859 vs 0.830 vs 0.906 in validation cohort. All predictive models have better calibration capabilities (p > 0.05).
Conclusions:
Predictive models combined CUS and elastography features would aid clinicians to make appropriate preoperative diagnosis of thyroid nodules among different risk stratification. The elastography parameter of VTIQ max has the priority in distinguishing thyroid malignancy with moderately suspicious (ACR TR4).
