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Machine learning applied to near-infrared spectra for clinical pleural effusion classification
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) co...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093263/ https://www.ncbi.nlm.nih.gov/pubmed/33941795 http://dx.doi.org/10.1038/s41598-021-87736-4 |
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author | Chen, Zhongjian Chen, Keke Lou, Yan Zhu, Jing Mao, Weimin Song, Zhengbo |
author_facet | Chen, Zhongjian Chen, Keke Lou, Yan Zhu, Jing Mao, Weimin Song, Zhengbo |
author_sort | Chen, Zhongjian |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8093263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80932632021-05-05 Machine learning applied to near-infrared spectra for clinical pleural effusion classification Chen, Zhongjian Chen, Keke Lou, Yan Zhu, Jing Mao, Weimin Song, Zhengbo Sci Rep Article 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. Nature Publishing Group UK 2021-05-03 /pmc/articles/PMC8093263/ /pubmed/33941795 http://dx.doi.org/10.1038/s41598-021-87736-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Chen, Zhongjian Chen, Keke Lou, Yan Zhu, Jing Mao, Weimin Song, Zhengbo Machine learning applied to near-infrared spectra for clinical pleural effusion classification |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093263/ https://www.ncbi.nlm.nih.gov/pubmed/33941795 http://dx.doi.org/10.1038/s41598-021-87736-4 |
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