机器学习算法能够预测用免疫检查点抑制剂治疗胃癌患者的预后
Hong-Wei Li1, Zi-Yu Zhu2, Yu-Fei Sun3
1Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, Heilongjiang Province, China.
World journal of gastroenterology
|November 4, 2024
概括
机器学习模型可以预测接受免疫检查点抑制剂 (ICI) 的晚期胃癌 (GC) 患者的结果. XGBoost模型在预测整体存活率和无进展存活率方面显示出准确性,突出显示患者营养状况作为预后因素.
科学领域:
- 在瘤学瘤学.
- 免疫治疗是一种免疫疗法.
- 机器学习在医学中的应用
背景情况:
- 免疫检查点抑制剂 (ICI) 为一些胃癌 (GC) 患者提供生存益处.
- 目前的预后标志物对于所有晚期GC病例来说都是不够的.
研究的目的:
- 确定预测ICI治疗的GC患者预后的生物标志物.
- 开发准确的预测模型,用于GC患者的结果.
主要方法:
- 利用了273名接受ICI的晚期GC患者的数据.
- 使用训练集开发机器学习模型 (XGBoost,后勤回归,决策树).
- 在单独的测试集上验证模型的预测能力.
主要成果:
- 确定了整体生存和无进展生存预测的关键特征.
- 极端梯度增强 (XGBoost) 模型在预测这两种结果方面都表现出了很高的准确性.
- 在一年内,112名患者死亡,129名病情进展.
结论:
- XGBoost模型有效地识别了可能从ICI中受益的GC患者.
- 患者的营养状况可以作为ICI上的GC患者的预后指标.
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