Construction and Validation of a Prediction Model for Identifying Clinical Risk Factors of Lateral Lymph Node Metastasis in Medullary Thyroid Carcinoma

构建和验证用于识别甲状腺髓样癌侧颈淋巴结转移临床危险因素的预测模型

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Abstract

PURPOSE: Medullary thyroid carcinoma (MTC) is a rare but highly invasive malignancy, especially in terms of cervical lymph node metastasis. However, the role of prophylactic lateral lymph node dissection (LLND) is still controversial. We hereby aim to explore the risk factors of lateral lymph node metastasis (LLNM) in patients with MTC to guide clinical practice. PATIENTS AND METHODS: The clinicopathological characteristics of patients with MTC from the Surveillance, Epidemiology, and End Results (SEER) Program and the Second Affiliated Hospital of Chongqing Medical University were reviewed and analyzed. Univariate and multivariate logistics regression analyses were used to screen the risk factors of LLNM in patients with MTC. RESULTS: Four variables, including male gender, multifocality, extrathyroidal invasion (EI), and large tumor size (all p < 0.05), were identified as potential independent factors of LLNM in patients with MTC. Based on these results, an individualized prediction model was subsequently developed with a satisfied C-index of 0.798, supported by both internal and external validation with a C-index of 0.816 and 0.896, respectively. We also performed the decision curve analysis (DCA) and calibration curve, which indicated a remarkable agreement in our model for predicting the risk of LLNM. CONCLUSION: We determined that various clinical characteristics, male gender, multifocality, EI, and large tumor size, were significantly associated with LLNM in patients with MTC. Thus, a validated prediction model utilizing readily available variables was successfully established to help clinicians make individualized clinical decisions on MTC management, especially regarding whether the LLND is necessary for patients with clinical negative lateral lymph node involvement and the frequency of follow-up without LLND.

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