在非小细胞肺癌中预测癌基因突变状态:系统性审查和元分析,特别关注基于人工智能的方法
Almudena Fuster-Matanzo1, Alfonso Picó-Peris2, Fuensanta Bellvís-Bataller2
1Quantitative Imaging Biomarkers in Medicine, Quibim, Valencia, Spain. almudenafuster@quibim.com.
European radiology
|September 8, 2025
概括
放射学和人工智能模型对非小细胞肺癌 (NSCLC) 癌基因突变的非侵入性预测充满希望. 虽然有效,但将这些与临床数据相结合,在NSCLC诊断的预测性能上提供了有限的改善.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 在非小细胞肺癌 (NSCLC) 中进行驱动突变分析需要使用非侵入性方法.
- 放射学和人工智能 (AI) 为非侵入性诊断工具提供了潜力.
研究的目的:
- 审查放射性化学的有效性,单独或与临床数据一起,预测NSCLC中的癌基因突变状态.
- 评估人工智能模型在使用放射学特征预测癌基因突变状态方面的表现.
主要方法:
- 使用放射学预测NSCLC中癌基因突变状态的研究的PRISMA-compliant文献审查.
- 使用CT衍生放射学特征的基于AI的模型的元分析,单独或与临床数据相结合.
- 超回归分析不同预测因素的影响.
主要成果:
- 包括124项研究,其中51项分析用于预测EGFR,ALK和KRAS突变.
- 基于放射学的AI模型显示了对EGFR,ALK和KRAS突变的不同灵敏度和错误阳性率.
- 对EGFR突变预测的组合模型显示灵敏度为0.806和FPR为0.315;临床数据提供了有限的改善.
结论:
- 基于放射学的模型为确定NSCLC中的瘤基因突变状态提供了一个可行的非侵入性替代方案.
- 需要进一步的研究来确定临床数据是否可以提高放射学模型的性能.
- 放射学和人工智能工具可以支持NSCLC中的分子分析,但需要验证才能进行临床整合.
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