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Updated: Jul 8, 2025
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
Published on: April 21, 2023
Study on diagnosing thyroid nodules of ACR TI-RADS 4-5 with multimodal ultrasound radiomics technology
Si-Rui Wang1,2, Pei-Shan Zhu1,2, Jun Li1,2
1NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, First Affiliated Hospital of Shihezi University, Shihezi, Xin Jiang, China.
Background:
Explore the feasibility of using the multimodal ultrasound (US) radiomics technology to diagnose American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) 4-5 thyroid nodules.
Method:
This study prospectively collected the clinical characteristics, conventional, and US elastography images of 100 patients diagnosed with ACR TI-RADS 4-5 nodules from May 2022 to 2023. Independent risk factors for malignant thyroid nodules were extracted and screened using methods such as the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model, and a multimodal US radiomics combined diagnostic model was established. Using a multifactorial LR analysis and a Rad-score rating, the predictive performance was validated and evaluated, and the final threshold range was determined to assess the clinical net benefit of the model.
Results:
In the training set, the US radiomics combined predictive model area under curve (AUC = 0.928) had higher diagnostic performance compared with clinical characteristics (AUC = 0.779), conventional US (AUC = 0.794), and US elastography model (AUC = 0.852). In the validation set, the multimodal US radiomics combined diagnostic model (AUC = 0.829) also had higher diagnostic performance compared with clinical characteristics (AUC = 0.799), conventional US (AUC = 0.802), and US elastography model (AUC = 0.718).
Conclusion:
Multi-modal US radiomics technology can effectively diagnose thyroid nodules of ACR TI-RADS 4-5, and the combination of radiomics signature and conventional US features can further improve the diagnostic performance.
