背景:肺癌的早期筛查和检测对于疾病的诊断和预后至关重要。在本文中,我们研究了血清拉曼光谱用于肺癌快速筛查的可行性。
方法:收集45例肺癌患者的拉曼光谱,45例肺部良性病变,45名健康志愿者然后应用支持向量机(SVM)算法建立肺癌诊断模型。此外,对15个独立个体进行了外部验证,包括5名肺癌患者,5例肺部良性病变患者,5健康对照
结果:诊断灵敏度,特异性,准确率为91.67%,92.22%,90.56%(肺癌与健康控制),92.22%,95.56%,93.33%(肺良性病变与健康)和80.00%,83.33%,80.83%(肺癌与良性肺病变),反复。在独立验证队列中,我们的模型显示所有样本分类正确.
结论:因此,这项研究表明,血清拉曼光谱分析技术与SVM算法相结合,在肺癌的无创检测中具有巨大的潜力。
BACKGROUND: Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.
METHODS: Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (
SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.
RESULTS: The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.
CONCLUSIONS: Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the
SVM algorithm has great potential for the noninvasive detection of lung cancer.