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Updated: Sep 20, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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机器学习算法与体液参数:一种可解释的框架,用于大脑脊髓液中恶性细胞查.

Xianfei Ye¹, Xinfeng Zhao², Yinyu Lou¹

¹Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, P.R. China.

Clinical chemistry and laboratory medicine

|May 29, 2025

概括

一个新的机器学习 (ML) 模型使用来自脑脊液 (CSF) 的常规血液学分析仪数据来有效地选恶性细胞. 这种方法为CSF样本中早期癌症检测提供了一种有希望的,可访问的方法.

关键词:

身体液体身体液体大脑脊髓液中的脑脊液.高光细胞是一种高光细胞.机器学习是机器学习.恶性细胞是恶性细胞.

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科学领域:

临床诊断临床诊断临床诊断
生物医学数据分析
血液学血液学血液学

背景情况:

脑脊液 (CSF) 分析对于诊断中枢神经系统 (CNS) 疾病至关重要.
对CSF进行恶性细胞的细胞学检查可能是劳动密集型的,需要专门的专业知识.
血液学分析仪提供易于获得的体液参数,可能具有诊断价值.

研究的目的:

开发和验证用于查CSF恶性细胞的机器学习 (ML) 模型.
为了这个查目的,利用血液学分析仪的常规体液参数.
通过内部和外部验证来评估模型的性能和通用性.

主要方法:

使用血液学分析仪的体液模式对643个脑脊液样本进行分析.
应用LASSO回归来从测量参数中确定预测生物标志物.
对6个ML算法的评估,重点是支持矢量机 (SVM).
使用夏普利添加式解释 (SHAP) 来实现模型的解释性.
外部验证使用136个额外的CSF样本.

主要成果:

在内部验证中,SVM模型实现了曲线下的面积 (AUC) 为0.899和灵敏度为0.827.
通过SHAP分析发现的关键预测因素包括高光细胞和单细胞百分比.
中位白细胞 (WBC) 和总核细胞 (TNC) 计数在细胞学阳性样本中明显较低.
外部验证证实了该模型与可比性能指标的通用性.

结论:

成功开发了一种ML模型,使用标准血液学参数预测CSF中的细胞学结果.
该模型展示了强大的性能和通用性,在独立的数据集上进行了验证.
这种方法提供了一个潜在的非侵入性查工具,用于CSF恶性细胞.