人工智能模型预测复发风险预测在早期非小细胞肺癌:一个系统性审查
Yichen Yang1, Hongbo He1,2, Chengyuan Yu1
1Department of Cardiothoracic Surgery, Heart and Vascular Center, Maastricht University Medical Center, Maastricht 6229 HX, The Netherlands.
预测模型对评估早期非小细胞肺癌复发风险有希望. 整合多式联络数据可以提高这种癌症的模型概括性和准确性.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 早期非小细胞肺癌 (NSCLC) 复发是一个重大的临床挑战.
- 准确预测术后复发对于个性化治疗策略至关重要.
研究的目的:
- 系统地评估早期NSCLC复发风险的预测模型.
- 评估整合不同数据模式对模型性能的影响.
主要方法:
- 在PubMed,Embase和Web of Science进行了系统的文献搜索.
- 包括17项研究,对研究特征,数据类型和绩效指标进行数据提取.
- 用预测模型预测偏差风险评估工具 (PROBAST) +AI评估偏差风险.
主要成果:
- 随机森林和随机生存森林模型显示,单一模式数据具有稳定性.
- 多模式数据集成显著提高了模型性能 (AUC 0.72-0.94).
- 在病理图像分析中,DeepRePath (XGBoost) 实现了0.94的AUC;图形神经网络在CT数据上表现良好 (AUC为0.785).
- 过度装配是一个常见的问题,几项研究表明在开发和验证阶段存在高偏差风险.
结论:
- 预测模型表明,在早期NSCLC中,有可能进行准确的复发风险评估.
- 多模式数据集成是提高这些模型的概括性和预测能力的关键.
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