一个基于机器学习的模型,用于预测中等和高风险差异化甲状腺癌的复发:来自2388名患者的回顾性单中心研究的见解
Yi Li1, Zimei Tang1, Anwen Ren1
1Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Frontiers in endocrinology
|July 2, 2025
概括
这项研究开发了一个机器学习模型来预测差异化甲状腺癌 (DTC) 复发. 随机森林模型准确地识别高风险患者,改进个性化治疗策略.
科学领域:
- 内分泌学 在内分泌学.
- 在瘤学瘤学.
- 数据科学数据科学数据科学
背景情况:
- 当前差异化甲状腺癌 (DTC) 准则提供了一个广泛的风险分层框架.
- 对于中等和高风险DTC患者,需要更精确的工具.
研究的目的:
- 为了确定与DTC复发相关的风险因素.
- 开发一个基于机器学习的DTC复发的预测模型.
主要方法:
- 对2,388名DTC患者进行了回顾性分析,数据分为培训 (1,910) 和验证 (478) 组.
- 单变量和多变量分析确定了预测因素.
- 训练和验证了六种机器学习模型,通过准确性,AUC和决策曲线分析来评估性能.
主要成果:
- 复发的独立风险因素包括腺体内传播,瘤大小,双边宫淋巴结参与,和哈西莫托的甲状腺炎.
- 发现正常/升高的TSH和多焦点结节具有保护作用.
- 随机森林模型实现了最高的性能 (训练精度:0.801;验证精度:0.808).
结论:
- 随机森林模型有效地预测了DTC的复发.
- 基于随机森林模型的在线计算器有助于个性化风险评估.
- 这个工具有助于DTC患者的临床决策.
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