It is significant to develop novel methods to diagnose the tumor status throughout chemotherapy. In the present work, we focused on identifying the elemental biomarkers of chemotherapy-treated and untreated tumor tissues by laser-induced breakdown spectroscopy (LIBS). The unsupervised algorithm as principal component analysis and three supervised algorithms including partial least squares discrimination analysis (PLS-DA), random forest (RF) as well as support vector machine (SVM) were used to develop efficient classification models. The average predictive accuracy was 90.74% via the PLS-DA, 88.89% via RF, and 83.33% via SVM, respectively. The results highlighted the spectral difference between chemotherapy-treated and untreated samples within the range of visible spectra between 300–700 nm. In the meantime, four major elements were found to contribute the classification over the following order: calcium > magnesium = copper > sodium. The results featured the importance of ...

中文翻译：

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激光诱导击穿光谱结合化学计量学方法区分对肿瘤化疗的元素反应性

开发新的方法来诊断整个化疗过程中的肿瘤状态具有重要意义。在目前的工作中，我们集中于通过激光诱导击穿光谱法（LIBS）来鉴定化学疗法治疗和未治疗的肿瘤组织的元素生物标志物。使用无监督算法作为主成分分析，并使用偏最小二乘判别分析（PLS-DA），随机森林（RF）和支持向量机（SVM）等三种监督算法来开发有效的分类模型。通过PLS-DA的平均预测准确度为90.74％，通过RF的平均预测准确度为88.89％，通过SVM的平均预测准确度为83.33％。结果突出显示了在300–700 nm之间可见光谱范围内，经化学处理的样品与未经处理的样品之间的光谱差异。同时，发现有四个主要元素按以下顺序进行分类：钙>镁=铜>钠。结果表明...的重要性