Machine learning applied to near-infrared spectra for clinical pleural effusion classification

doi:10.1038/s41598-021-87736-4

HFCAS OpenIR

	Machine learning applied to near-infrared spectra for clinical pleural effusion classification
	Chen, Zhongjian 1,2,3,4; Chen, Keke 1,2,3,4; Lou, Yan 5; Zhu, Jing 1,2,3; Mao, Weimin 1,2,3; Song, Zhengbo 1,2,3
	2021-05-03
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
通讯作者	Mao, Weimin(maowm1218@163.com) ; Song, Zhengbo(zbszjch@163.com)
摘要	Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method based on FTIR near-infrared spectroscopy (NIRS) combined with machine learning strategy for clinical pleural effusion classification. NIRS spectra were recorded for 47 MPE samples and 35 BPE samples. The sample data were randomly divided into train set (n=62) and test set (n=20). Partial least squares, random forest, support vector machine (SVM), and gradient boosting machine models were trained, and subsequent predictive performance were predicted on the test set. Besides the whole spectra used in modeling, selected features using SVM recursive feature elimination algorithm were also investigated in modeling. Among those models, NIRS combined with SVM showed the best predictive performance (accuracy: 1.0, kappa: 1.0, and AUC(ROC): 1.0). SVM with the top 50 feature wavenumbers also displayed a high predictive performance (accuracy: 0.95, kappa: 0.89, AUC(ROC): 0.99). Our study revealed that the combination of NIRS and machine learning is an innovative, rapid, and convenient method for clinical pleural effusion classification, and worth further evaluation.
DOI	10.1038/s41598-021-87736-4
关键词[WOS]	CANCER ; SPECTROSCOPY ; BIOMARKERS ; DIAGNOSIS ; MARKER ; CEA
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[81672315] ; National Natural Science Foundation of China[81802276] ; National Natural Science Foundation of China[81302840] ; Key R&D Program Projects in Zhejiang Province[2017C04G1360498]
项目资助者	National Natural Science Foundation of China ; Key R&D Program Projects in Zhejiang Province
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000657422500001
出版者	NATURE RESEARCH
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/123851
专题	中国科学院合肥物质科学研究院
通讯作者	Mao, Weimin; Song, Zhengbo
作者单位	1.Univ Chinese Acad Sci, Chinese Acad Sci, Canc Hosp, Banshandong Rd 1, Hangzhou 310000, Zhejiang, Peoples R China 2.Zhejiang Canc Hosp, Banshandong Rd 1, Hangzhou 310000, Zhejiang, Peoples R China 3.Chinese Acad Sci, Inst Canc & Basic Med IBMC, Hangzhou, Peoples R China 4.Zhejiang Univ, Coll Pharmaceut Sci, Yuhangtang Rd 866, Hangzhou 310000, Zhejiang, Peoples R China 5.Hangzhou Hosp, Zhejiang Med & Hlth Grp, Intens Care Unit, Banshan Kangjian Rd 1, Hangzhou 310000, Zhejiang, Peoples R China
推荐引用方式 GB/T 7714	Chen, Zhongjian,Chen, Keke,Lou, Yan,et al. Machine learning applied to near-infrared spectra for clinical pleural effusion classification[J]. SCIENTIFIC REPORTS,2021,11.
APA	Chen, Zhongjian,Chen, Keke,Lou, Yan,Zhu, Jing,Mao, Weimin,&Song, Zhengbo.(2021).Machine learning applied to near-infrared spectra for clinical pleural effusion classification.SCIENTIFIC REPORTS,11.
MLA	Chen, Zhongjian,et al."Machine learning applied to near-infrared spectra for clinical pleural effusion classification".SCIENTIFIC REPORTS 11(2021).