基于组件建模的胃肠癌拉曼光谱的智能诊断算法
Mingkun Wang1, Juan Li2, Wenbo Mo1
1Department of Materials Science and Technology, Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang 621900, China. rclfkit@caep.cn.
Analytical methods : advancing methods and applications
|October 21, 2025
概括
这项研究提出了一个非侵入性的拉曼光谱和卷积神经网络 (CNN) 框架,用于早期检测胃肠癌. 该方法准确地识别了组织中的分子变化,从而能够精确地区分良性和恶性病例.
科学领域:
- 生物医学工程 生物医学工程
- 频谱学是一种光谱学.
- 计算生物学 计算生物学
背景情况:
- 早期的胃肠道 (GI) 癌症诊断至关重要,但由于缺乏分子敏感性的侵入性常规方法而受到限制.
- 拉曼光谱提供了非侵入性的分子指纹,但在生物样本中的光谱重叠是一个挑战.
研究的目的:
- 开发一种结合拉曼光谱和卷积神经网络 (CNN) 的诊断框架,用于定量光谱成分分析.
- 改善检测和区分良性和恶性胃肠组织.
主要方法:
- 在100,000个模拟光谱上训练了一种改进的CNN回归模型,该模型基于829个GI组织和五个纯生化成分的拉曼光谱.
- 定量分析了DNA,三蛋白,基因素,原蛋白和活性蛋白的相对比例.
- 采用LightGBM分类模型,使用定量分子特征进行组织歧视.
主要成果:
- 在CNN模型准确量化生化比例 (R^2:0.91-0.98).
- 恶性组织显示显著更高的DNA,原和动氨酸,以及较低的三烯和素系数 (P < 0.01).
- 在一个独立的测试组中,LightGBM分类器实现了97.2%的准确性,90%的灵敏性,98.1%的特异性和0.973 AUC.
结论:
- 开发的框架量化模拟了关键的分子变化,以便有效地区分良性和恶性肠道组织.
- 这种非侵入性方法表明,对于消化道癌查具有显著的临床潜力.
- 该策略可用于其他复杂的生物分析和诊断.
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