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

Abstract 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...

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Autores principales:	Zhongjian Chen, Keke Chen, Yan Lou, Jing Zhu, Weimin Mao, Zhengbo Song
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3b349c99e6ef40e2a6dfbd1c8cdf50b5
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spelling	oai:doaj.org-article:3b349c99e6ef40e2a6dfbd1c8cdf50b52021-12-02T14:42:52ZMachine learning applied to near-infrared spectra for clinical pleural effusion classification10.1038/s41598-021-87736-42045-2322https://doaj.org/article/3b349c99e6ef40e2a6dfbd1c8cdf50b52021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87736-4https://doaj.org/toc/2045-2322Abstract 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 AUCROC: 1.0). SVM with the top 50 feature wavenumbers also displayed a high predictive performance (accuracy: 0.95, kappa: 0.89, AUCROC: 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.Zhongjian ChenKeke ChenYan LouJing ZhuWeimin MaoZhengbo SongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhongjian Chen Keke Chen Yan Lou Jing Zhu Weimin Mao Zhengbo Song Machine learning applied to near-infrared spectra for clinical pleural effusion classification
description	Abstract 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 AUCROC: 1.0). SVM with the top 50 feature wavenumbers also displayed a high predictive performance (accuracy: 0.95, kappa: 0.89, AUCROC: 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.
format	article
author	Zhongjian Chen Keke Chen Yan Lou Jing Zhu Weimin Mao Zhengbo Song
author_facet	Zhongjian Chen Keke Chen Yan Lou Jing Zhu Weimin Mao Zhengbo Song
author_sort	Zhongjian Chen
title	Machine learning applied to near-infrared spectra for clinical pleural effusion classification
title_short	Machine learning applied to near-infrared spectra for clinical pleural effusion classification
title_full	Machine learning applied to near-infrared spectra for clinical pleural effusion classification
title_fullStr	Machine learning applied to near-infrared spectra for clinical pleural effusion classification
title_full_unstemmed	Machine learning applied to near-infrared spectra for clinical pleural effusion classification
title_sort	machine learning applied to near-infrared spectra for clinical pleural effusion classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3b349c99e6ef40e2a6dfbd1c8cdf50b5
work_keys_str_mv	AT zhongjianchen machinelearningappliedtonearinfraredspectraforclinicalpleuraleffusionclassification AT kekechen machinelearningappliedtonearinfraredspectraforclinicalpleuraleffusionclassification AT yanlou machinelearningappliedtonearinfraredspectraforclinicalpleuraleffusionclassification AT jingzhu machinelearningappliedtonearinfraredspectraforclinicalpleuraleffusionclassification AT weiminmao machinelearningappliedtonearinfraredspectraforclinicalpleuraleffusionclassification AT zhengbosong machinelearningappliedtonearinfraredspectraforclinicalpleuraleffusionclassification
_version_	1718389561408618496

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

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