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胃食道癌的呼吸生物标志物:识别,量化和诊断建模

Yuke Ren¹, Fei Wang², Ziyi Zhu³

¹Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, State Key Laboratory of Clean Energy Utilization (Zhejiang University), Hangzhou, 310027, China.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

|April 15, 2025

概括

分析呼吸中的挥发性有机化合物 (VOC) 是一种有希望的,非侵入性的方法,用于早期检测食道癌症. 这种方法实现了高精度,提高了这一关键疾病的诊断能力.

科学领域:

在瘤学瘤学.
分析化学分析化学
生物标志物生物标志物

背景情况:

食道癌症是一个重大的全球健康挑战,死亡率很高.
目前的诊断方法往往是侵入性的和昂贵的,阻碍了早期检测.
非常需要非侵入性诊断工具来改善患者的治疗结果.

研究的目的:

调查呼出的挥发性有机化合物 (VOC) 作为食道癌的非侵入性生物标志物的潜力.
开发和验证一种用于通过呼吸分析检测食道癌症的诊断模型.
评估VOC分析的准确性和特异性,以早期诊断食道癌症.

主要方法:

气色谱-质谱法 (GC-MS) 用于分析呼吸样本.
从80名食道癌患者和60名健康对照人群收集了呼吸样本.
开发了一个基于神经网络的诊断模型,并根据生理因素进行了优化.

主要成果:

在患者和对照组之间观察到VOC度 (例如,乙,乙醇,异烯) 的显著差异.
最初的诊断模型在区分食道癌症方面达到90.3%的准确性.
结合生理因素的优化模型达到92.4%的准确性和93.1%的特异性.

结论:

呼出VOC分析是一种可行的非侵入性,具有成本效益的工具,用于早期检测食道癌症.
开发的诊断模型表现出高精度和特异性,优于先前的研究.
这种基于呼吸的方法在改善食道癌查和诊断方面具有显著的前景.

关键词:

呼吸分析呼吸分析食道癌是食道癌的一个类型.神经网络的神经网络的神经网络挥发性有机化合物挥发性有机化合物

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