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An enrichment model using regular health examination data for early detection of colorectal cancer
OBJECTIVE: Challenges remain in current practices of colorectal cancer (CRC) screening, such as low compliance, low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data. METHODS: The study population...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736654/ https://www.ncbi.nlm.nih.gov/pubmed/31564811 http://dx.doi.org/10.21147/j.issn.1000-9604.2019.04.12 |
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author | Shi, Qiang Gao, Zhaoya Wu, Pengze Heng, Fanxiu Lei, Fuming Wang, Yanzhao Gao, Qingkun Zeng, Qingmin Niu, Pengfei Li, Cheng Gu, Jin |
author_facet | Shi, Qiang Gao, Zhaoya Wu, Pengze Heng, Fanxiu Lei, Fuming Wang, Yanzhao Gao, Qingkun Zeng, Qingmin Niu, Pengfei Li, Cheng Gu, Jin |
author_sort | Shi, Qiang |
collection | PubMed |
description | OBJECTIVE: Challenges remain in current practices of colorectal cancer (CRC) screening, such as low compliance, low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data. METHODS: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees (CART) algorithm. Receiver operating characteristic (ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening. RESULTS: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers (age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve (AUC) of the CART model was 0.88 [95% confidence interval (95% CI), 0.87−0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2% (95% CI, 58.1%−66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value (1.6%) than fecal immunochemical test (0.3%). CONCLUSIONS: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare. |
format | Online Article Text |
id | pubmed-6736654 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-67366542019-09-27 An enrichment model using regular health examination data for early detection of colorectal cancer Shi, Qiang Gao, Zhaoya Wu, Pengze Heng, Fanxiu Lei, Fuming Wang, Yanzhao Gao, Qingkun Zeng, Qingmin Niu, Pengfei Li, Cheng Gu, Jin Chin J Cancer Res Original Article OBJECTIVE: Challenges remain in current practices of colorectal cancer (CRC) screening, such as low compliance, low specificities and expensive cost. This study aimed to identify high-risk groups for CRC from the general population using regular health examination data. METHODS: The study population consist of more than 7,000 CRC cases and more than 140,000 controls. Using regular health examination data, a model detecting CRC cases was derived by the classification and regression trees (CART) algorithm. Receiver operating characteristic (ROC) curve was applied to evaluate the performance of models. The robustness and generalization of the CART model were validated by independent datasets. In addition, the effectiveness of CART-based screening was compared with stool-based screening. RESULTS: After data quality control, 4,647 CRC cases and 133,898 controls free of colorectal neoplasms were used for downstream analysis. The final CART model based on four biomarkers (age, albumin, hematocrit and percent lymphocytes) was constructed. In the test set, the area under ROC curve (AUC) of the CART model was 0.88 [95% confidence interval (95% CI), 0.87−0.90] for detecting CRC. At the cutoff yielding 99.0% specificity, this model’s sensitivity was 62.2% (95% CI, 58.1%−66.2%), thereby achieving a 63-fold enrichment of CRC cases. We validated the robustness of the method across subsets of test set with diverse CRC incidences, aging rates, genders ratio, distributions of tumor stages and locations, and data sources. Importantly, CART-based screening had the higher positive predictive value (1.6%) than fecal immunochemical test (0.3%). CONCLUSIONS: As an alternative approach for the early detection of CRC, this study provides a low-cost method using regular health examination data to identify high-risk individuals for CRC for further examinations. The approach can promote early detection of CRC especially in developing countries such as China, where annual health examination is popular but regular CRC-specific screening is rare. AME Publishing Company 2019-08 /pmc/articles/PMC6736654/ /pubmed/31564811 http://dx.doi.org/10.21147/j.issn.1000-9604.2019.04.12 Text en Copyright © 2019 Chinese Journal of Cancer Research. All rights reserved. http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Original Article Shi, Qiang Gao, Zhaoya Wu, Pengze Heng, Fanxiu Lei, Fuming Wang, Yanzhao Gao, Qingkun Zeng, Qingmin Niu, Pengfei Li, Cheng Gu, Jin An enrichment model using regular health examination data for early detection of colorectal cancer |
title | An enrichment model using regular health examination data for early detection of colorectal cancer |
title_full | An enrichment model using regular health examination data for early detection of colorectal cancer |
title_fullStr | An enrichment model using regular health examination data for early detection of colorectal cancer |
title_full_unstemmed | An enrichment model using regular health examination data for early detection of colorectal cancer |
title_short | An enrichment model using regular health examination data for early detection of colorectal cancer |
title_sort | enrichment model using regular health examination data for early detection of colorectal cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736654/ https://www.ncbi.nlm.nih.gov/pubmed/31564811 http://dx.doi.org/10.21147/j.issn.1000-9604.2019.04.12 |
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