揭示结肠直肠癌:一种in vivo拉曼光谱方法与深度学习分析相结合
Maria Anthi Kouri1,2, Maria Karnachoriti3, Ellas Spyratou1
12nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.
International journal of molecular sciences
|December 9, 2023
概括
这项研究表明,便携式拉曼光谱仪可以在活小鼠中准确检测结直肠癌. 一个深度学习模型实现了高精度,为临床癌症诊断铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 医学光谱学 医学光谱学
- 在瘤学瘤学.
背景情况:
- 拉曼光谱为生物研究提供高特异性和灵敏度.
- 便携式系统和光纤探针使得体内组织分析成为可能.
- 准确地区分健康和癌症组织对于诊断至关重要.
研究的目的:
- 为了评估一个便携式拉曼探针光谱仪在小鼠体内直肠癌局部化.
- 用拉曼光谱学将恶性瘤与正常组织边缘区分开来.
- 评估深度学习模型的性能,用于从活体动物组织中分类拉曼光谱.
主要方法:
- 在患有结直肠癌的免疫抑制小鼠身上使用便携式拉曼探针光谱仪.
- 拉曼光谱进行了预处理,并应用了主要成分分析 (PCA) 来进行组织歧视.
- 基于1D卷积神经网络 (1D-CNN) 的转移学习模型被用于光谱分类.
主要成果:
- 主要成分分析突出了恶性和正常组织之间的生化差异.
- 1D-CNN模型在分类结直肠组织方面实现了89.9%的准确性和91.4%的精度.
- 在体内成功局部化结直肠癌恶性瘤被证明.
结论:
- 便携式拉曼光谱是体内癌症检测的可行工具.
- 深度学习模型在生物医学应用中提高了拉曼光谱的准确性.
- 这项技术在临床癌症诊断和手术内边缘评估方面显著有前途.
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