Study on diagnosing thyroid nodules of ACR TI‐RADS 4–5 with multimodal ultrasound radiomics technology

• Published in: Journal of clinical ultrasound Vol. 52; no. 3; pp. 274 - 283
• Main Authors: Wang, Si‐Rui, Zhu, Pei‐Shan, Li, Jun, Chen, Ming, Cao, Chun‐Li, Shi, Li‐Nan, Li, Wen‐Xiao
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2024; Wiley Subscription Services, Inc
• Subjects: ACR TI‐RADS; Data systems; Diagnostic systems; Nodules; Performance evaluation; Performance prediction; Prediction models; Radiomics; Regression models; Risk factors; Thyroid; Thyroid gland; Ultrasonic imaging; Ultrasound; ultrasound‐based radiomics; thyroid; ultrasound-based radiomics; ACR TI-RADS; ultrasound
• ISSN: 0091-2751; 1097-0096; 1097-0096
• DOI: 10.1002/jcu.23625

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. This study demonstrates the effectiveness of multimodal ultrasound (US) radiomics technology in diagnosing ACR‐TI‐RADS 4–5 thyroid nodules. The combination of radiomics signature and conventional US features enhances diagnostic performance, offering a promising approach for accurate and efficient diagnosis of malignant nodules. Workflow of radiomics analysis and model building. ROC, receiver operating characteristic.