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基于衰减全反射傅里叶变换红外光谱(ATR-FTIR)和化学计量学的肺癌诊断

Diagnosis of Lung Cancer by ATR-FTIR Spectroscopy and Chemometrics.

作者信息

Yang Xien, Ou Quanhong, Qian Kai, Yang Jianru, Bai Zhixun, Yang Weiye, Shi Youming, Liu Gang

机构信息

School of Physics and Electronic Information, Yunnan Normal University, Kunming, China.

Department of Thoracic Surgery, The First People's Hospital of Yunnan Province, Kunming, China.

出版信息

Front Oncol. 2021 Sep 30;11:753791. doi: 10.3389/fonc.2021.753791. eCollection 2021.

Abstract

Lung cancer is the leading cause of cancer-related death in the world. Early diagnosis has great significance for the survival of patients with lung cancer. In this paper, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics was used to study the serum samples from patients with lung cancer and healthy people. The results of spectral band area comparison showed that the concentrations of protein, lipid and nucleic acids molecules in serum of patients with lung cancer were increased compared with those in healthy people. The original spectra were preprocessed to improve the accuracy of principal component regression (PCR) and partial least squares-discriminant analysis (PLS-DA) models. PLS-DA results for first derivative spectral data in nucleic acids (1250-1000cm) band showed 80% sensitivity, 91.89% specificity and 87.10% accuracy with high of 0.8949 and of 0.8153, low RMSEC of 0.3136 and RMSEV of 0.4180. It is shown that ATR-FTIR spectroscopy combined with chemometrics might be developed as a simple method for clinical screening and diagnosis of lung cancer.

摘要

肺癌是全球癌症相关死亡的主要原因。早期诊断对肺癌患者的生存具有重要意义。本文采用衰减全反射傅里叶变换红外(ATR-FTIR)光谱结合化学计量学方法,对肺癌患者和健康人的血清样本进行研究。光谱带面积比较结果显示,肺癌患者血清中蛋白质、脂质和核酸分子的浓度高于健康人。对原始光谱进行预处理,以提高主成分回归(PCR)和偏最小二乘判别分析(PLS-DA)模型的准确性。核酸(1250-1000cm)波段一阶导数光谱数据的PLS-DA结果显示,灵敏度为80%,特异性为91.89%,准确率为87.10%,高为0.8949,低为0.8153,RMSEC为0.3136,RMSEV为0.4180。结果表明,ATR-FTIR光谱结合化学计量学方法有望发展成为一种简单的肺癌临床筛查和诊断方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2d1/8515056/52c442b688b1/fonc-11-753791-g001.jpg

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