通过机器学习开发和内部验证早期结直肠癌的基于免疫的预后建模通过机器学习
Xiu Chen1, Yong Wang1, Heng-Yang Shen1
1Department of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China.
World journal of clinical oncology
|January 29, 2026
概括
早期结直肠癌 (EOCRC) 风险可以使用疾病阶段和自然杀手 (NK) 细胞水平来预测. 该模型有助于个性化EOCRC治疗,并识别潜在的免疫治疗点.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 数据科学数据科学数据科学
背景情况:
- 早期结直肠癌 (EOCRC) 是一个越来越令人担忧的疾病,年轻人预后不佳.
- 在EOCRC中循环免疫细胞的预后作用尚不清楚.
研究的目的:
- 开发机器学习模型来预测EOCRC患者的治疗结果.
- 使用外周免疫标记物进行预后分层.
主要方法:
- 对123名经过彻底切除的EOCRC患者进行了回顾性分析.
- 流细胞计用于评估外围免疫细胞标记物.
- 机器学习模型 (Cox模型,随机生存森林) 开发用于风险预测.
主要成果:
- 疾病阶段和自然杀手 (NK) 细胞百分比是关键预测因素.
- 结合阶段和NK细胞的考克斯模型显示出卓越的性能 (一致性指数0.693).
- 高风险患者 (III/IV阶段,高NK细胞) 的5年无进展生存率明显降低.
结论:
- 一个双因素模型 (阶段,NK细胞) 为EOCRC风险分层提供了适度的准确性.
- 系统性NK细胞功能障碍与EOCRC进展有关.
- 建议对这种预后模型进行进一步的外部验证.
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