Luo Huaichao, Zu Ruiling, Li Lintao, Deng Yao, He Shuya, Yin Xing, Zhang Kaijiong, He Qiao, Yin Yu, Yin Gang, Yao Dezhong, Wang Dongsheng
Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
Department of Radiation Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
iScience. 2023 Apr 23;26(5):106693. doi: 10.1016/j.isci.2023.106693. eCollection 2023 May 19.
It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.
已证明拉曼光谱强度可用于诊断肺癌患者。然而,拉曼光谱在识别肺结节患者中的应用鲜有研究。在本研究中,我们发现健康参与者以及良性和恶性肺结节患者血清样本的拉曼光谱存在显著差异。根据方差分析测试结果,开发了一种支持向量机(SVM)模型,用于基于波点对拉曼光谱进行分类。当将SVM模型用于区分良性和恶性个体时,其表现良好,曲线下面积(AUC)中位数为0.89。与三种常见临床模型相比,SVM模型显示出更好的鉴别能力,并为参与者带来了更多净益处,在小尺寸结节中也表现出色。因此,拉曼光谱可能是一种侵入性较小且成本较低的液体活检方法。