甲状腺癌的发病率正在快速增长，手术是其最重要的治疗方法。对于单侧cN0甲状腺乳头状癌患者是否要清扫对侧中央淋巴结目前仍在争论中。在这里，我们的目标是利用人口统计学和临床​​数据提供基于机器学习的对侧中央淋巴结转移预测模型。对来自武汉协和医院的2225例单侧cN0乳头状甲状腺癌患者进行回顾性研究。比较对侧中央淋巴结转移与非对侧中央淋巴结转移患者的临床和病理特征。基于这些患者构建了六种机器学习模型，并使用准确性、敏感性、特异性、受试者工作特征下面积和决策曲线分析进行比较。然后使用中国分化型甲状腺癌研究的数据验证所选模型。所有统计分析和模型构建均采用R软件进行。男性、最大直径大于1cm、多灶性、同侧中央淋巴结转移、年龄小于50岁是对侧中央淋巴结转移的独立危险因素。随机森林模型的表现优于其他模型，并在外部验证队列中得到验证。构建了网络计算器。对侧中央淋巴结清扫应考虑性别、最大直径、多灶性、同侧中央淋巴结转移和年龄。基于随机森林模型的网络计算器可能有助于临床决策。版权所有 © 2024 任、朱、吴、明、阮、徐、黄。

The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data.2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software.Male, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed.Gender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.Copyright © 2024 Ren, Zhu, Wu, Ming, Ruan, Xu and Huang.