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使用机器学习进行淋巴瘤细胞分类的电化学辅助散射成像系统

Linyan Xie^1,2, Ning Zhang¹, Kai Yang¹

¹School of Medical Engineering and School of Mathematical Medicine, Xinxiang Medical University, Xinxiang 453003, China.

Biomedical optics express

|August 14, 2025

概括

本研究介绍了一种电化学辅助散射成像系统 (ESIS),用于增强淋巴瘤细胞分类. 这种新的双模式方法显著提高了诊断准确度,有助于个性化癌症治疗.

科学领域:

生物医学工程生物医学工程
在瘤学瘤学.
分析化学分析化学

背景情况:

淋巴瘤是全球流行的一种恶性瘤,强调了需要准确和早期诊断方法的需要.
目前用于淋巴瘤的诊断技术可以改进,以获得更好的患者结果和个性化治疗策略.

研究的目的:

开发和验证一种电化学辅助散射成像系统 (ESIS),用于精确的淋巴瘤细胞分类.
使用多模式方法提高淋巴瘤亚型分化的准确性.

主要方法:

将散射成像与使用光纤探头和3D rGO-Ti3C2-MWCNTs复合电极进行电化学测量的整合.
同时监测淋巴瘤细胞释放的过氧化 (H2O2).
支持矢量机 (SVM) 算法的应用用于数据分析和分类.

主要成果:

ESIS在分类性能上取得了显著的改善,HMy2.CIR细胞的曲线下的面积 (AUC) 从0.79增加到0.97.
双模态方法的准确性达到了90%,仅仅超越散射成像.
观察到淋巴瘤亚型的增强分化能力.

结论:

开发的ESIS为准确的淋巴瘤细胞分类和亚型分化提供了一个有前途的工具.
这种双模系统有可能通过改进的诊断来推进个性化癌症治疗.

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