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一种基于机器学习的拉曼光谱方法,用于非侵袭性乳腺炎的非侵袭性诊断

Yongqi Li^1,2,3,4, Haoran Zhang^1,2,3,4, Yining Jia^1,2,3,4

¹Breast Center, The Second Qilu Hospital of Shandong University, 247 Beiyuan St, Jinan, Shandong Province, 250033, People's Republic of China.

Analytical and bioanalytical chemistry

|January 16, 2026

概括

早期诊断非乳腺性乳腺炎 (NPM) 是非常重要的. 对外围血液单核细胞 (PBMC) 的拉曼光谱与机器学习相结合,为快速的NPM检测和分析提供了一种有前途的新方法.

科学领域:

生物医学光学生物医学光学
分子诊断学分子诊断
计算生物学计算生物学

背景情况:

非毛囊性乳腺炎 (NPM) 的诊断需要及时和准确的方法.
周围血液单核细胞 (PBMC) 是关键的炎症媒介和潜在的生物标志物.
目前对PBMCs的检测方法需要改进,用于液体活检应用.

研究的目的:

研究拉曼光谱法,以鉴定NPM患者PBMC中的分子变化.
开发基于PBMC分析的机器学习模型来预测NPM.
探索PBMC液体活检在NPM诊断中的潜力.

主要方法:

从NPM患者和健康对照组中收集PBMC.
使用拉曼光谱分析PBMCs的分子概况.
应用机器学习算法 (PCA,LDA,PLSDA,SVM) 用于诊断建模.

主要成果:

拉曼光谱学成功地描述了PBMC中的分子变化.
机器学习模型实现了高诊断精度 (AUC > 0.93).
使用PBMC分析证明了区分NPM患者与对照者的可行性.

结论:

通过拉曼光谱的PBMC分析是NPM检测的可行方法.
使用PBMC,拉曼光谱和机器学习的液体活检显示了NPM的显著诊断潜力.
这种综合方法为早期NPM诊断和管理提供了新的机会.

关键词:

诊断诊断诊断诊断诊断机器学习是机器学习.非毛囊性乳腺炎 (non-puerperal mastitis) 是一种非毛囊性乳腺炎.周围血液中的单核细胞.拉曼光谱法拉曼光谱法拉曼光谱法

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