The Value of a Combined Model Based on Ultra-Radiomics and Multi-modal Ultrasound in the Benign-Malignant Differentiation of C-TIRADS 4A Thyroid Nodules: A

Shuai Cui¹Qifan Liu¹Hailong Wang¹Husha Li¹Wei Li¹Chenlong Li²Leilei Bi³Yang Mu⁴Wenjing Guo¹Jundong Yao^1*Zhoulong Zhang¹

• ¹The First Affiliated Hospital of Henan University of Science and Technology, Luoyang, Henan Province, China
• ²Pingmei Shenma Medical Group General Hospital, Pingdingshan, Henan Province, China
• ³XiangYang Hospital of Traditional Chinese Medicine, Xiangyang, China
• ⁴People's Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, Xinjiang Uyghur Region, China

Objective: To establish a combined model based on ultrasound radiomics combined with multimodal ultrasound and evaluate its value in diagnosing benign and malignant nodules classified as Chinese-Thyroid Imaging Report and Data System (C-TIRADS) 4A. Methods: Prospective collection of data from 446 patients with thyroid nodules classified as C-TIRADS 4A between December 2023 and August 2024. Based on the enrollment timeline, patients were divided into a training set (n=312) and a test set (n=134) in a 7:3 ratio. Using clinical information, multimodal ultrasound features, and radiomics features, a radiomics model was constructed using the Random Forest (RF) machine learning algorithm. Logistic regression was employed to develop the multimodal ultrasound model and the combined model. The predictive efficiency and accuracy of these models were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). The diagnostic efficacy of junior physicians assisted by the ultrasound radiomics model was compared with that of senior physicians. DeLong's test was performed to compare the diagnostic performance of the models. Results: Multivariate analysis revealed that age (≤51 years), Sound Touch Elastography mean stiffness (STE Mean), orientation (vertical), margin (blurred), and margin (irregular) were independent risk factors for papillary thyroid carcinoma, and the multimodal ultrasound model was established. Based on 17 ultrasound radiomics features, a radiomics model was constructed using the RF machine learning algorithm. The combined model was developed by combining the two aforementioned models. In the training set, the areas under the curve (AUC) of the multimodal ultrasound model, ultrasound radiomics model, and combined model were 0.852, 0.940 and 0.956, respectively. In the test set, the AUC were 0.804, 0.832 and 0.863, respectively. DeLong's test showed that the combined model performed best in the training set, and in the test set, the combined model outperformed the multimodal ultrasound model but showed no significant difference compared to the radiomics model. DCA indicated that the combined model achieved higher net benefits within a specific threshold probability range (0.15-0.90).The combined model exhibits robust diagnostic capability in distinguishing benign from malignant thyroid nodules classified as C-TIRADS 4A.