Chang Liu ¹ Shangjie Yang ^2,3 Tian Xue ⁴ Qian Zhang ^2,3 Yanjing Zhang ³ Yufang Zhao ³ Guolin Yin ³ Xiaohui Yan ³ Liping Liu ^3*

• ¹ Xian Central Hospital, Xian, China
• ² Shanxi Medical University, Taiyuan, Shanxi Province, China
• ³ First Hospital of Shanxi Medical University, Taiyuan, China
• ⁴ Shanxi Maternal and Child Health Care Hospital, Shanxi Children's Hospital, Taiyuan, Shanxi Province, China

Background: PTC (papillary thyroid cancer) is a lymphotropic malignancy associated with cervical lymph node metastasis (CLNM，including central and lateral LNM), which compromises the effect of treatment and prognosis of patients. Accurate preoperative identification will provide valuable reference information for the formulation of diagnostic and treatment strategies. The aim of this study was to develop and validate a clinical-multimodal ultrasound radiomics model for predicting CLNM of PTC. Methods: One hundred sixty-four patients with PTC who underwent treatment at our hospital between March 2016 and December 2021 were included in this study. The patients were grouped into a training cohort (n=115) and a validation cohort (n=49). Radiomic features were extracted from the conventional ultrasound (US), contrast-enhanced ultrasound (CEUS) and strain elastography-ultrasound (SE-US) images of patients with PTC. Multivariate logistic regression analysis was used to identify the independent risk factors. FAE software was used for radiomic feature extraction and the construction of different prediction models. The diagnostic performance of each model was evaluated and compared in terms of the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value (NPV) and positive predictive value (PPV). RStudio software was used to develop the decision curve and assess the clinical value of the prediction model. Results: The clinical-multimodal ultrasound radiomics model developed in this study can successfully detect CLNM in PTC patients. A total of 3720 radiomic features (930 features per modality) were extracted from the ROIs of the multimodal images, and 15 representative features were ultimately screened. The combined model showed the best prediction performance in both the training and validation cohorts, 2with AUCs of 0.957 (95% CI: 0.918 -0.987) and 0.932 (95% CI: 0.822 -0.984), respectively. Decision curve analysis revealed that the combined model was superior to the other models.The clinical-multimodal ultrasound radiomics model constructed with multimodal ultrasound radiomic features and clinical risk factors has favorable potential and high diagnostic value for predicting CLNM in PTC patients.