染色恐惧性细胞癌的个性化生存预测:开发基于机器学习的网络工具
Sakhr Alshwayyat1,2,3, Noor Almasri4, Yamen Alshwaiyat5
1Kern Medical (UCLA David Geffen School of Medicine Affiliate), Bakersfield, CA, USA.
International urology and nephrology
|August 18, 2025
概括
这项研究确定了转移和瘤大小是染色恐惧性细胞癌 (ChRCC) 存活的关键预测因素. 机器学习模型的开发旨在创建一个新的网络工具,用于个性化预测CHRCC患者的存活率.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 染色恐惧性细胞癌 (ChRCC) 是一种罕见的癌亚型,研究有限.
- 了解预后因素和治疗结果对于CHRCC患者管理至关重要.
研究的目的:
- 为了确定慢性肺癌患者生存的显著预后因素.
- 开发机器学习 (ML) 模型来预测CHRCC存活率.
- 创建第一个基于网络的工具,用于在ChRCC.CC中实时生存预测.
主要方法:
- 从SEER数据库 (2000-2020年) 分析了10,700名慢性慢性癌症患者.
- 考克斯回归和卡普兰-梅尔生存分析以确定预后变量.
- 开发和验证5个ML算法 (AUC-ROC) 用于5年生存预测.
主要成果:
- 转移和瘤大小被确定为生存的重要预测因素.
- 亚整体切除与最高的生存率相关.
- 化疗和放射治疗显示出更糟糕的生存结果,特别是转移.
结论:
- 已经确定了CHRCC生存的关键预后因素.
- 一个基于机器学习的网络工具提供了个性化的生存预测.
- 转移,瘤大小和外科手术方法 (亚整体切除术) 对于慢性肺癌的结果至关重要.
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