基于机器学习的模型,用于预测皮肤状甲状腺癌中对侧中央淋巴结的转移,而皮肤状甲状腺癌是靠近地峡的
Lin Wang1, Yue Han1, Chaohui Wang1
1Department of Thyroid and Breast Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Frontiers in endocrinology
|January 26, 2026
概括
一个新的机器学习模型准确地预测了皮肤状甲状腺癌 (PTC) 患者的逆侧中央淋巴结转移. 这个工具有助于个性化手术决定的淋巴结解剖.
科学领域:
- 在瘤学瘤学.
- 手术瘤学手术瘤学
- 医疗信息学 医疗信息学
背景情况:
- 发自甲状腺峡的乳头甲状腺癌 (PTC) 具有向对侧中央淋巴结 (Cont-CLNs) 转移的高风险.
- 准确的风险评估对于PTC患者个性化治疗策略至关重要.
研究的目的:
- 开发和验证一个个性化的预测模型,用于Cont-CLNs转移在PTC与地峡附近.
- 利用机器学习算法来提高预测准确度.
主要方法:
- 对1672名PTC患者的回顾性分析,重点关注397名靠近地峡的患者.
- 使用Boruta算法和LASSO回归的特征选择.
- 开发和评估七个机器学习模型,包括随机森林,使用SHAP进行解释性.
主要成果:
- 在靠近地峡的PTC中,Cont-CLNs转移的发生率明显高 (33%),而非地峡的PTC则高 (12%).
- 确定的关键预测因素包括手术前CT和超声评估,甲状腺外延伸,双侧CLN转移和瘤大小.
- 随机森林模型实现了高性能 (AUC 0.942培训,0.861验证),手术前CT评估是最有影响力的预测因素.
结论:
- 一个基于机器学习的模型有效地预测了Cont-CLNs在PTC中的转移风险.
- 这个模型是个性化手术决策的宝贵工具,优化淋巴结剖析程度.
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