The Value of Multimodal Ultrasound Based on Machine Learning Algorithms in the Diagnosis of Benign and Malignant Thyroid Nodules of TI-RADS Category 4: A Single-Center Retrospective Study.
Authors
Affiliations (3)
Affiliations (3)
- Department of Ultrasound, The 901st Hospital of the Joint Logistics Support Force of PLA, Hefei, 230031, China.
- Department of Ultrasound, Peking University Third Hospital, No. 49 North Garden Road, Haidian District, Beijing, 100191, China.
- Center of Minimally Invasive Interventional Therapy, Dongfang Hospital Affiliated to Tongji University, No. 150, Jimo Road, Pudong District, Shanghai, 200120, China.
Abstract
<p> Introduction: Ultrasound is routinely used for thyroid nodule diagnosis, yet distinguishing benign from malignant TI-RADS category 4 nodules remains challenging. This study has integrated two-dimensional ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) features via machine learning to improve diagnostic accuracy for these nodules. </p> <p> Methods: A total of 117 TI-RADS 4 thyroid nodules from 108 patients were included and classified as benign or malignant based on pathological results. Two-dimensional ultrasound, CEUS, and SWE were compared. Predictive features were selected using LASSO regression. Feature importance was further validated using Random Forest, SVM, and XGBoost algorithms. A logistic regression model was constructed and visualized as a nomogram. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). </p> <p> Results: Malignant nodules exhibited significantly elevated serum FT3, FT4, FT3/FT4, TSH, and TI-RADS scores compared to benign lesions. Key imaging discriminators included unclear boundaries, aspect ratio ≥1, low internal echo, microcalcifications on ultrasound; enhancement degree, circumferential enhancement, and excretion on CEUS; and elevated SWE values (Emax, Emean, Esd, etc.) and altered CEUS quantitative parameters (PE, WiR, WoR, etc.) (all P< 0.05). A nomogram integrating four optimal predictors, including Emax, FT4, TI-RADS, and ΔPE, demonstrated robust predictive performance upon validation by ROC, calibration, and DCA curve analysis. </p> <p> Discussion: The nomogram incorporating Emax, FT4, TI-RADS, and ΔPE showed high predictive accuracy, particularly for papillary carcinoma in TI-RADS 4 nodules. Its applicability may, however, be constrained by the single-center retrospective design and limited pathological coverage. </p> <p> Conclusion: The multimodal ultrasound-based machine learning model effectively predicted malignancy in TI-RADS category 4 thyroid nodules. </p>.