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Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers

BACKGROUND: Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum tumour markers has been frequently discussed. However, the risk–benefit outcomes appear...

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Autores principales: Wang, Hsin-Yao, Hsieh, Chia-Hsun, Wen, Chiao-Ni, Wen, Ying-Hao, Chen, Chun-Hsien, Lu, Jang-Jih
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927114/
https://www.ncbi.nlm.nih.gov/pubmed/27355357
http://dx.doi.org/10.1371/journal.pone.0158285
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author Wang, Hsin-Yao
Hsieh, Chia-Hsun
Wen, Chiao-Ni
Wen, Ying-Hao
Chen, Chun-Hsien
Lu, Jang-Jih
author_facet Wang, Hsin-Yao
Hsieh, Chia-Hsun
Wen, Chiao-Ni
Wen, Ying-Hao
Chen, Chun-Hsien
Lu, Jang-Jih
author_sort Wang, Hsin-Yao
collection PubMed
description BACKGROUND: Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum tumour markers has been frequently discussed. However, the risk–benefit outcomes appear to be unfavourable for patients because of the low sensitivity and specificity. In this study, cancer screening models based on multiple serum tumour markers were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbour (KNN), and logistic regression, to improve the screening performance for multiple cancers in a large asymptomatic population. METHODS: AFP, CEA, CA19-9, CYFRA21-1, and SCC were determined for 20 696 eligible individuals. PSA was measured in men and CA15-3 and CA125 in women. A variable selection process was applied to select robust variables from these serum tumour markers to design cancer detection models. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the curve, and Youden index of the models based on single tumour markers, combined test, and machine learning methods were compared. Moreover, relative risk reduction, absolute risk reduction (ARR), and absolute risk increase (ARI) were evaluated. RESULTS: To design cancer detection models using machine learning methods, CYFRA21-1 and SCC were selected for women, and all tumour markers were selected for men. SVM and KNN models significantly outperformed the single tumour markers and the combined test for men. All 3 studied machine learning methods outperformed single tumour markers and the combined test for women. For either men or women, the ARRs were between 0.003–0.008; the ARIs were between 0.119–0.306. CONCLUSION: Machine learning methods outperformed the combined test in analysing multiple tumour markers for cancer detection. However, cancer screening based solely on the application of multiple tumour markers remains unfavourable because of the inadequate PPV, ARR, and ARI, even when machine learning methods were incorporated into the analysis.
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spelling pubmed-49271142016-07-18 Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers Wang, Hsin-Yao Hsieh, Chia-Hsun Wen, Chiao-Ni Wen, Ying-Hao Chen, Chun-Hsien Lu, Jang-Jih PLoS One Research Article BACKGROUND: Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum tumour markers has been frequently discussed. However, the risk–benefit outcomes appear to be unfavourable for patients because of the low sensitivity and specificity. In this study, cancer screening models based on multiple serum tumour markers were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbour (KNN), and logistic regression, to improve the screening performance for multiple cancers in a large asymptomatic population. METHODS: AFP, CEA, CA19-9, CYFRA21-1, and SCC were determined for 20 696 eligible individuals. PSA was measured in men and CA15-3 and CA125 in women. A variable selection process was applied to select robust variables from these serum tumour markers to design cancer detection models. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the curve, and Youden index of the models based on single tumour markers, combined test, and machine learning methods were compared. Moreover, relative risk reduction, absolute risk reduction (ARR), and absolute risk increase (ARI) were evaluated. RESULTS: To design cancer detection models using machine learning methods, CYFRA21-1 and SCC were selected for women, and all tumour markers were selected for men. SVM and KNN models significantly outperformed the single tumour markers and the combined test for men. All 3 studied machine learning methods outperformed single tumour markers and the combined test for women. For either men or women, the ARRs were between 0.003–0.008; the ARIs were between 0.119–0.306. CONCLUSION: Machine learning methods outperformed the combined test in analysing multiple tumour markers for cancer detection. However, cancer screening based solely on the application of multiple tumour markers remains unfavourable because of the inadequate PPV, ARR, and ARI, even when machine learning methods were incorporated into the analysis. Public Library of Science 2016-06-29 /pmc/articles/PMC4927114/ /pubmed/27355357 http://dx.doi.org/10.1371/journal.pone.0158285 Text en © 2016 Wang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Hsin-Yao
Hsieh, Chia-Hsun
Wen, Chiao-Ni
Wen, Ying-Hao
Chen, Chun-Hsien
Lu, Jang-Jih
Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers
title Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers
title_full Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers
title_fullStr Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers
title_full_unstemmed Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers
title_short Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers
title_sort cancers screening in an asymptomatic population by using multiple tumour markers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927114/
https://www.ncbi.nlm.nih.gov/pubmed/27355357
http://dx.doi.org/10.1371/journal.pone.0158285
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