预测结肠直肠癌和先进结肠直肠息肉的机器学习:系统性审查和元分析
Sheza Malik1, Arsalan Naqvi2, Bettina G Tenorio3
1Rochester General Hospital, Rochester, NY.
Journal of clinical gastroenterology
|May 28, 2025
概括
机器学习模型在预测结肠直肠癌 (CRC) 和晚期结肠直肠息肉 (ACP) 风险方面表现有前途,其聚合灵敏度为0.832和特异性为0.802. 对于常规临床应用,需要进一步的标准化.
科学领域:
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 在医疗保健中对于结直肠癌 (CRC) 检测和诊断越来越重要.
- 人工智能辅助的结肠镜和预测模型正在增强诊断能力.
研究的目的:
- 评估ML模型在结肠镜检查前预测CRC和晚期结肠直肠多 (ACP) 风险方面的有效性.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 使用CHARMS检查清单提取数据.
- 进行元分析以评估CRC和腺瘤预测的模型性能.
主要成果:
- 包括14项研究,中位数为3618名患者.
- 在方法和结果方面表现出相当大的异质性 (AUROC 0.6-1).
- 聚合灵敏度:0.832 (95% CI:0.755-0.889);特异性:0.802 (95% CI:0.722-0.863);整体AUROC:0.883.3 总体AUROC:0.883.3 总体AUROC:0.883.3 总体AUROC:0.883.3 总体AUROC:0.883.3 总体AUROC:0.832
结论:
- ML在CRC和ACP诊断中发挥着重要作用.
- 常规的ML使用可以优化高风险患者的结肠镜转诊.
- 可变性需要标准化的方法和进一步的研究.
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