Construction of a Predictive Model for T1-stage Low-risk Papillary Thyroid Carcinoma with Central Lymph Node Metastasis Using Ultrasound Radiomics Combined with Clinical Radiomics

Background Papillary thyroid carcinoma (PTC) is the most common tumor subtype of thyroid cancer and approximately 30–90% of patients with PTC exhibit occult central lymph node metastasis (CLNM) on postoperative pathological examination. This study aimed to explore the diagnostic efficacy and clinical application value of combining ultrasound radiomics features with clinical features to construct predictive models for patients with papillary thyroid carcinoma (PTC) and central lymph node metastasis (CLNM). Methods This study included the retrospective data from total 191 PTC patients hospitalized between June 2020 and June 2022 (training set: 134, validation set: 57). Additionally, 46 patients were included in the prospective validation set. Clinical features affecting CLNM in patients with PTC were identified using univariate and multivariate analyses. Logistic regression models were constructed based on clinical and radiomics features, individually and combined. The diagnostic efficacies of the three models were compared using receiver operating characteristic curves, and a nomogram was constructed for visualization. The clinical utility of the model was evaluated using decision curve analysis (DCA) and calibration curves. Results Male sex, unclear or irregular margins, and microcalcifications were independent risk factors in the clinical radiomics predictive model. The area under the curve (AUC) for the training, validation, and prospective validation sets was 0.740, 0.656, and 0.626, respectively. Twelve ultrasound radiomics features were selected to construct the radiomics model (AUC: 0.794, 0.720, and 0.766, respectively). The combined model demonstrated AUCs of 0.850, 0.750, and 0.786, for training, validation and prospective validation set respectively. The DCA and calibration curves indicated that the combined model had a better diagnostic efficiency and clinical utility. Conclusion This study presents a combined predictive model based on ultrasound radiomics and clinical features that can effectively predict preoperative CLNM in patients with PTC cN0 T1 stage, demonstrating its clinical applicability.