一个新的结肠直肠癌查框架,具有特征可解释性,用于识别结肠镜检查的高风险人群
Mingshan Li1,2, Yangming Gong3, Yi Pang3
1Department of Electronic Engineering, Fudan University, Shanghai, China.
Journal of gastroenterology and hepatology
|May 14, 2024
概括
一个新的结肠直肠癌查框架使用可解释的机器学习识别了高风险的个人进行结肠镜检查. 这种方法有效地针对那些最有可能患有结直肠癌的人,优化诊断资源.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 结肠直肠癌 (CRC) 风险评估对于早期检测和治疗至关重要.
- 制定有效的查策略对于降低CRC负担至关重要.
- 目前的选方法可以改进,以更好地分层风险.
研究的目的:
- 开发和验证可解释的特征查框架,用于识别患结直肠癌高风险人群.
- 推对被开发的框架认定为高风险的个人进行结肠镜检查.
- 提高结直肠癌查计划的效率和准确性.
主要方法:
- 使用来自上海 (2013-2015) 的大量培训队伍 (1,252,605名参与者) 开发了一个查框架.
- 为了特征解释性,纳入了沙普利的附加解释值.
- 机器学习算法用于构建风险评估模型,并使用采样方法解决类不平衡问题.
- 模型在359,462名参与者的外部队列上得到了验证.
主要成果:
- 经过验证的模型表现出高性能,灵敏度>0.734,特异性>0.790,AUC范围从0.808到0.859.
- 最好的模型预测CRC患病率为0.059%在低风险组和1.056%在高风险组.
- 将结肠镜定向于模型识别的高风险组 (占总数的14.36%) 可以检测出74.86%的CRC病例,大大改善了资源分配.
结论:
- 一个新的,经过验证的框架有效地识别了患结直肠癌高风险的个体.
- 该框架提供了一种可解释的风险评估方法,有助于临床决策.
- 根据框架确定的高风险个体应进行结肠镜检查,以便及时诊断和治疗.
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