细胞癌通过减弱总反射里埃变换进行歧视 干燥人体尿液的红外光谱学和机器学习技术
Bogdan Adrian Buhas1,2, Lucia Ana-Maria Muntean3, Guillaume Ploussard4
1Department of Urology, Medicover Hospital, 323T Principala St., 407062 Suceagu, Romania.
International journal of molecular sciences
|September 28, 2024
概括
干燥尿液的减弱总反射-里埃变换红外光谱 (ATR-FTIR) 显示了区分细胞癌 (RCC) 的潜力. 这种方法在RCC患者中确定了较低的肌水平,通过机器学习实现了82%的准确性.
科学领域:
- 生物医学光谱学 生物医学光谱学
- 分析化学 分析化学
- 泌尿外科瘤学 泌尿外科瘤学
背景情况:
- 细胞癌 (RCC) 是一种常见的癌症,由于无症状呈现,经常被发现迟到.
- 早期发现RCC对于改善患者的治疗结果至关重要.
- 尿液分析为癌症生物标志物发现提供了一种非侵入性方法.
研究的目的:
- 调查减弱总反射 - 里埃变换红外光谱 (ATR-FTIR) 的实用性,以区分细胞癌 (RCC) 与健康个体.
- 使用ATR-FTIR分析干尿样本的光谱差异.
- 评估基于尿液FTIR光谱的RCC分类的机器学习算法.
主要方法:
- 从49名RCC患者和39名健康捐赠者的干尿样本使用ATR-FTIR光谱分析.
- 他们将Spectra与人造尿液和单个尿液成分进行了比较,以确定振动频段.
- 主要组件分析-线性差异分析 (PCA-LDA) 和支持向量机 (SVM) 用于分类.
主要成果:
- 尿素是主要的光谱成分,可识别的酸,酸和尿酸的峰值.
- 观察到肌素峰值强度的统计学上显著差异,这些强度在RCC患者中较低.
- 对于RCC,PCA-LDA实现了82%的区分精度,超过SVM的76%.
结论:
- 尿液ATR-FTIR光谱学与机器学习相结合,显示出作为RCC检测的非侵入性方法的希望.
- 在RCC患者中观察到的尿水平的降低需要进一步调查.
- 在更大的队列和涉及其他尿道疾病的研究中进行验证是必要的,以确认这种方法的临床适用性.
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