乳腺癌患者发展多种原发性癌症的危险因素:一项回顾性研究和机器学习模型的建立/测试
Yudi Jin1, Tong Su1, Yanjia Fan2
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
BMC medical informatics and decision making
|July 27, 2025
概括
这项研究确定了乳腺癌患者多种原发性癌症 (MPC) 的风险因素. 机器学习模型预测MPC风险,帮助乳腺癌幸存者的个性化临床决策.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 乳腺癌是全球主要的恶性瘤,其中很大一部分患者发展为次要原发性瘤.
- 了解多种原发性癌症 (MPC) 的风险因素对于改善患者的治疗结果和指导临床管理至关重要.
研究的目的:
- 调查与乳腺癌患者中MPCs发展相关的风险因素.
- 开发和验证用于评估MPC风险的预测模型.
主要方法:
- 来自监测,流行病学和最终结果 (SEER) 数据库的大型队列的分析.
- 开发和评估物流回归和随机森林机器学习模型.
- 使用的患者数据包括人口统计,瘤特征和治疗方式.
主要成果:
- 模型被训练和测试在一个平衡的数据集的乳腺癌患者与一个原发性乳腺癌 (OPBC) 和MPCs.
- 后勤回归模型显示出强大的预测性能 (AUC培训:0.902,测试:0.886).
- 随机森林模型显示出出色的预测准确性 (AUC培训:0.955,测试:0.874),并为风险分层开发了一个名录.
结论:
- 确定了导致乳腺癌患者MPC发展的关键风险因素.
- 机器学习模型,特别是随机森林,为个性化风险评估提供了一个实用的工具.
- 基于Nomogram的风险分层显示出显著的预后差异,支持临床实用性.
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