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通过深度神经网络模型的肺瘤歧视CanDo通过支气管洗中DNA甲基化进行支气管洗

Zezhong Yu¹, Jieyi Li^2,3, Yi Deng¹

¹Department of Respiratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

|June 17, 2024

概括

一个新的分子测试,CanDo,使用支气管洗液准确区分肺癌. 这种方法比传统的支气管镜评估具有更高的灵敏度和特异性,改善了肺癌诊断.

科学领域:

在瘤学瘤学.
分子诊断学分子诊断
生物信息学是一种生物信息学.

背景情况:

肺部瘤的支气管镜诊断面临着难以接近的病变或患者的禁忌的限制.
精确的分子分辨对于有效的肺癌管理至关重要.

研究的目的:

为了识别甲基化标记物,用于分子分辨肺瘤.
开发和验证使用支气管洗液 (BWF) 诊断肺瘤的新型分类模型 (CanDo).

主要方法:

分析和HumanMethylation450数据库验证确定了甲基化标记物.
对118个BWF标本进行了有针对性的甲基化测序.
一个深度神经网络算法和递归特征消除交叉验证开发了CanDo分类模型.

主要成果:

使用11个甲基化特征,包括8个TBR1基因标记物的CanDo模型,实现了98.6%的灵敏度和97.8%的特异性.
支气管镜快速现场评估 (支气管镜-ROSE) 显示性能较低 (87.7%的灵敏度,80%的特异性).
在33名患者的验证证实了CanDo的潜力,特别是在挑战支气管镜ROSE的复杂病例中.

结论:

使用BWF,CanDo提供了一种非常准确的,非侵入性的方法来进行肺瘤歧视.
这种分子方法补充了支气管镜检查,有助于诊断和分层管理肺瘤.
CanDo证明了改善肺癌诊断工作流程的巨大潜力.

关键词:

细胞细胞细胞计算方法的计算方法.疾病疾病疾病

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lncRNA - Long Non-coding RNAs

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