一个由机器学习优化的肌素-CAR复合指数 (CCAR) 用于癌症白内障的预后
Heyang Zhang1,2,3,4, Chong Li5, Qiuyi Chen6
1Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, National Clinical Research Center for Digestive Diseases, Beijing Digestive Disease Center, Beijing Key Laboratory for Precancerous Lesion of Digestive Diseases, Beijing, China.
Journal of cachexia, sarcopenia and muscle
|November 11, 2025
概括
使用肌素和CAR的新型癌风险指数 (CCAR) 准确预测癌患者的存活率. 这种易于访问的工具有助于风险分层和个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 生物标志物 生物标志物
- 预测 预测 预测 预测 预测
背景情况:
- 癌症缓解症显著影响患者的预后和生活质量.
- 目前缺乏可靠的生物标志物来预测癌症缓解症的生存率.
研究的目的:
- 开发和验证一种新的指数,用于预测癌症缓解症患者的生存率.
- 为了确定癌症缓解症生存的关键预测因素.
主要方法:
- 对1367名癌症缓解症患者进行了回顾性队列研究.
- 拉索回归确定了肌素 (Cr) 作为一个关键预测因子.
- 癌症卡切西亚风险 (CCAR) 指数是使用Cr和CAR通过随机森林模型构建的.
主要成果:
- 与传统标志物相比,CCAR指数在培训,内部和外部验证队列中表现出优越的预后性能.
- 高CCAR组患者的整体存活率 (OS) 显著降低.
- CCAR被证实是OS的独立预后因素.
结论:
- CCAR指数提供了一个强大而易于使用的工具,用于预测癌症缓解症的生存结果.
- 基于网络的CCAR计算器提高了风险分层和个性化治疗的临床适用性.
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