拉曼光谱用于新生和非新生CAM结肠瘤的分类
B Esteves1, S Pimenta1,2, M J Maciel1,2
1CMEMS-UMinho, Department of Industrial Electronics, University of Minho, Guimarães, Portugal.
Heliyon
|September 16, 2024
概括
拉曼光谱学有效地区分瘤与非瘤结肠瘤,使用肉胆膜 (CAM) 模型. 这种方法达到93%的准确性,为瘤分化提供了一个有前途的工具,具有最小的伦理问题.
科学领域:
- 生物医学光学 生物医学光学
- 癌症研究 癌症研究
- 频谱学是一种光谱学.
背景情况:
- 区分瘤和非瘤结肠瘤对于准确的诊断和治疗至关重要.
- 现有的方法可能在速度,可访问性或道德考虑方面存在局限性.
- 肉胆膜 (CAM) 模型为研究人类瘤发育提供了一个可行的体内平台.
研究的目的:
- 评估拉曼光谱在新生和非新生结肠瘤之间的区分方面的潜力.
- 开发和验证一种方法来处理和分析来自CAM植入瘤的拉曼光谱数据.
- 评估机器学习算法在基于拉曼光谱的瘤类型分类中的准确性.
主要方法:
- 人类结肠细胞系 (瘤的RKO,非瘤的NCM460) 植入胚胎的CAM.
- 拉曼光谱从瘤样本中采集,使用785nm激发激光.
- 光谱数据经过了基线校正和文物删除,随后进行了主要组件分析 (PCA) 和机器学习分类.
主要成果:
- 主要成分分析 (PCA) 得分清楚地区分了瘤和非瘤结肠瘤.
- 一个机器学习分类模型在区分这两种瘤类型方面取得了93%的高精度.
- 经过验证的方法证明了用于瘤分类的有效光谱处理和分析.
结论:
- 拉曼光谱与PCA和机器学习相结合,提供了一种强大的方法来区分瘤和非瘤结肠瘤.
- 在癌症研究中,CAM模型作为一个有效和道德考虑的平台来验证光谱技术的有效性.
- 这种方法为潜在的未来临床应用在结肠瘤诊断中提供了一个快速和易于使用的工具.
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