Differentiating Mummified Thyroid Nodules From Papillary Thyroid Carcinoma: A Machine Learning Approach Using Multi-Modal Ultrasound Radiomics.

OBJECTIVES

This study aimed to establish a machine-learning model that integrates contrast-enhanced ultrasound (CEUS) radiomics, conventional ultrasound (US) radiomics and clinical features for distinguishing mummified thyroid nodules (MTNs) from papillary thyroid carcinomas (PTCs). The goal is to provide strategies for subsequent clinical decision-making.

METHODS

This study included 120 PTCs and 84 MTNs. Clinical information was obtained from the patients to identify independent risk factors. Radiomics features were extracted from the images. After feature selection, Logistic Regression (LR) and support vector machine (SVM) algorithms were used to build various models. Finally, the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the diagnostic performance and clinical utility of the models. A nomogram was used to visualize the final model.

RESULTS

The developed US+CEUS radiomics model demonstrated the best diagnostic performance. With seven CEUS and three US features, it achieved an area under the curve (AUC) of 0.936 in the training set and 0.881 in the test set. The DCA indicated that the US+CEUS radiomics model achieved a higher net benefit within the threshold probability range of 0.07-0.97.

CONCLUSIONS

The US+CEUS radiomics model shows high diagnostic value in differentiating PTCs from MTNs with the model dominated by CEUS features. However, combining it with clinical features did not improve the diagnostic performance of the model, so we should combine modalities appropriately according to the specific disease.