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评估基于DESI成像脂质数据构建的预测分类器在质谱平台上的通用性

Rachel J DeHoog^1,2, Monica Lin², Gregory Roman³

¹Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, USA.

Journal of the American Society for Mass Spectrometry

|June 9, 2023

概括

这项研究表明,使用DESI脂质数据的预测模型可以在不同的质谱平台上可靠地分类甲状腺细针吸收 (FNA) 样本,有助于癌症诊断.

科学领域:

生物医学科学生物医学科学
分析化学分析化学
在瘤学瘤学.

背景情况:

甲状腺细针吸收 (FNA) 活检对于诊断甲状腺结节至关重要.
开发FNA分析的强大和可通用的预测模型对于准确的癌症分类至关重要.
脱电喷射电离 (DESI) 质谱学为组织分析提供了分子分析能力.

研究的目的:

评估基于DESI脂质数据开发的预测分类器的概括性,用于甲状腺FNA分析.
评估这些分类器在不同高分辨率质谱平台和用户的性能.
验证这些模型的适用性,以区分甲状腺癌和良性组织.

主要方法:

使用了两个高性能质谱仪 (飞行时间和轨道速度) 与不同的DESI成像源.
从甲状腺组织样本生成分子概况.
将以前发表的统计模型应用于独立数据集和临床FNA.
预测结果与已确定的诊断之间的评估一致性.

主要成果:

来自不同平台的分子形状显示出类似的趋势,尽管离子丰度有所不同.
预测分类器在30个成像平台中的24个样本中达成一致.
分类者证明了对另外六个临床FNA的临床诊断的同意.

结论:

来自DESI脂质数据的统计分类器适用于各种高分辨率质谱平台.
这些发现支持使用基于DESI的脂质组分类器进行甲状腺FNA分析和分类.
模型的通用性提高了它们在甲状腺癌诊断中临床应用的潜力.

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