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Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms

Background: Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early...

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Autores principales: Wang, Hsin-Yao, Chen, Chun-Hsien, Shi, Steve, Chung, Chia-Ru, Wen, Ying-Hao, Wu, Min-Hsien, Lebowitz, Michael S., Zhou, Jiming, Lu, Jang-Jih
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352838/
https://www.ncbi.nlm.nih.gov/pubmed/32492934
http://dx.doi.org/10.3390/cancers12061442
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author Wang, Hsin-Yao
Chen, Chun-Hsien
Shi, Steve
Chung, Chia-Ru
Wen, Ying-Hao
Wu, Min-Hsien
Lebowitz, Michael S.
Zhou, Jiming
Lu, Jang-Jih
author_facet Wang, Hsin-Yao
Chen, Chun-Hsien
Shi, Steve
Chung, Chia-Ru
Wen, Ying-Hao
Wu, Min-Hsien
Lebowitz, Michael S.
Zhou, Jiming
Lu, Jang-Jih
author_sort Wang, Hsin-Yao
collection PubMed
description Background: Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early diagnosis of cancer. Methods: ML-based algorithms, including a cancer screening algorithm and a secondary organ of origin algorithm, were developed and validated using a large real world dataset (RWD) from asymptomatic individuals undergoing routine cancer screening at a Taiwanese medical center between May 2001 and April 2015. External validation was performed using data from the same period from a separate medical center. The data set included tumor marker values, age, and gender from 27,938 individuals, including 342 subsequently confirmed cancer cases. Results: Separate gender-specific cancer screening algorithms were developed. For men, a logistic regression-based algorithm outperformed single-marker and other ML-based algorithms, with a mean area under the receiver operating characteristic curve (AUROC) of 0.7654 in internal and 0.8736 in external cross validation. For women, a random forest-based algorithm attained a mean AUROC of 0.6665 in internal and 0.6938 in external cross validation. The median time to cancer diagnosis (TTD) in men was 451.5, 204.5, and 28 days for the mild, moderate, and high-risk groups, respectively; for women, the median TTD was 229, 132, and 125 days for the mild, moderate, and high-risk groups. A second algorithm was developed to predict the most likely affected organ systems for at-risk individuals. The algorithm yielded 0.8120 sensitivity and 0.6490 specificity for men, and 0.8170 sensitivity and 0.6750 specificity for women. Conclusions: ML-derived algorithms, trained and validated by using a RWD, can significantly improve tumor marker-based screening for multiple types of early stage cancers, suggest the tissue of origin, and provide guidance for patient follow-up.
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spelling pubmed-73528382020-07-15 Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms Wang, Hsin-Yao Chen, Chun-Hsien Shi, Steve Chung, Chia-Ru Wen, Ying-Hao Wu, Min-Hsien Lebowitz, Michael S. Zhou, Jiming Lu, Jang-Jih Cancers (Basel) Article Background: Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early diagnosis of cancer. Methods: ML-based algorithms, including a cancer screening algorithm and a secondary organ of origin algorithm, were developed and validated using a large real world dataset (RWD) from asymptomatic individuals undergoing routine cancer screening at a Taiwanese medical center between May 2001 and April 2015. External validation was performed using data from the same period from a separate medical center. The data set included tumor marker values, age, and gender from 27,938 individuals, including 342 subsequently confirmed cancer cases. Results: Separate gender-specific cancer screening algorithms were developed. For men, a logistic regression-based algorithm outperformed single-marker and other ML-based algorithms, with a mean area under the receiver operating characteristic curve (AUROC) of 0.7654 in internal and 0.8736 in external cross validation. For women, a random forest-based algorithm attained a mean AUROC of 0.6665 in internal and 0.6938 in external cross validation. The median time to cancer diagnosis (TTD) in men was 451.5, 204.5, and 28 days for the mild, moderate, and high-risk groups, respectively; for women, the median TTD was 229, 132, and 125 days for the mild, moderate, and high-risk groups. A second algorithm was developed to predict the most likely affected organ systems for at-risk individuals. The algorithm yielded 0.8120 sensitivity and 0.6490 specificity for men, and 0.8170 sensitivity and 0.6750 specificity for women. Conclusions: ML-derived algorithms, trained and validated by using a RWD, can significantly improve tumor marker-based screening for multiple types of early stage cancers, suggest the tissue of origin, and provide guidance for patient follow-up. MDPI 2020-06-01 /pmc/articles/PMC7352838/ /pubmed/32492934 http://dx.doi.org/10.3390/cancers12061442 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Hsin-Yao
Chen, Chun-Hsien
Shi, Steve
Chung, Chia-Ru
Wen, Ying-Hao
Wu, Min-Hsien
Lebowitz, Michael S.
Zhou, Jiming
Lu, Jang-Jih
Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms
title Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms
title_full Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms
title_fullStr Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms
title_full_unstemmed Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms
title_short Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms
title_sort improving multi-tumor biomarker health check-up tests with machine learning algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352838/
https://www.ncbi.nlm.nih.gov/pubmed/32492934
http://dx.doi.org/10.3390/cancers12061442
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