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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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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. |
format | Online Article Text |
id | pubmed-4927114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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