Malignant risk prediction of cystic-solid thyroid nodules using a comprehensive model integrating clinical and ultrasound features, ultrasound radiomics, and deep transfer learning.

Background: The risk of malignancy in cystic-solid thyroid nodules (CSTN) varies greatly and may be underestimated. This study aimed to explore the value of a comprehensive model that integrates deep transfer learning (DTL), ultrasound radiomics, and clinical, and ultrasound features in predicting the risk of malignancy of CSTN. Methods: A retrospective analysis was conducted on 278 patients with CSTN confirmed by pathology from the First Affiliated Hospital of Guangxi Medical University from January 2023 to December 2023. Radiomics features were manually extracted from ultrasound images, and DTL features were extracted using deep learning networks. The least absolute shrinkage and selection operator (LASSO) regression was utilized to select non-zero coefficient features from radiomics and DTL features. The comprehensive model nomogram was constructed using a logistic regression algorithm that integrates clinical, ultrasound features, deep learning, and radiomics features. The predictive performance was assessed using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), calibration curves, and decision curves. Subsequently, DeLong testing was performed for comparative analysis of the AUC, with parameter estimates including a 95% confidence interval (CI), and a P value of less than 0.05 was considered statistically significant. Results: The AUC of each model was compared, revealing that the comprehensive model outperformed the individual models in predicting the malignancy risk of CSTN, demonstrating good predictive performance with sensitivity and specificity of 87.50% and 82.90%, respectively. Additionally, the AUC of the comprehensive model in the testing set was 0.913 (95% CI: 0.844-0.982), which was higher than the radiomics model (0.913 Conclusions: The novel comprehensive model based on ultrasound demonstrates excellent performance in predicting the malignancy risk of CSTN, providing clinicians with a preoperative non-invasive screening method to predict the malignancy risk of CSTN.