机器学习模型对结肠癌存活率的比较:预测模型方法
Reuben Adatorwovor1, Motolani E Ogunsanya2, Bin Huang3
1Department of Biostatistics, College of Public Health, University of Kentucky, 760 Rose street, Suite 208H, Lexington, KY, 40536, United States, 1 859-218-0959.
JMIR cancer
|November 26, 2025
概括
机器学习模型通过识别治疗和吸烟等关键风险因素,显著改善结肠癌存活率预测. 这些先进的方法比传统方法提供了更好的风险分层.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 结肠癌是全球癌症死亡的主要原因之一.
- 传统的生存模型与复杂的风险因素相互作用作斗争.
- 机器学习 (ML) 为生存预测提供了先进的功能.
研究的目的:
- 用肯塔基癌症注册数据对结肠癌生存率估计的ML模型进行比较.
- 确定影响子组内生存的关键风险因素.
- 加强结肠癌患者的风险分层和治疗计划.
主要方法:
- 对33,825例结肠癌病例 (2010-2022) 的回顾性分析.
- 与传统方法 (Cox,Kaplan-Meier) 相比,ML模型 (极端梯度增强,随机生存森林,LASSO,弹性网) 的比较.
- 使用Brier分数,一致性指数和其他指标进行评估;对缺失数据进行多次归算.
主要成果:
- ML模型确定了年龄,治疗,节点,阶段,吸烟和并发症作为关键预测因素.
- 没有治疗与3.24倍高的死亡风险相关;吸烟者有24%的高风险.
- 随机生存森林和LASSO模型在预测准确性方面表现优于考克斯模型 (整体一致指数为0.8146).
结论:
- ML有效地识别了重要的结肠癌存活风险因素.
- 关键预测因素包括淋巴结状况,年龄,治疗,瘤大小,学年级,吸烟,地区和婚姻状况.
- ML通过提供子组特定的风险因素洞察力来增强个性化护理.
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