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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...

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Autores principales: Shi, Qiang, Gao, Zhaoya, Wu, Pengze, Heng, Fanxiu, Lei, Fuming, Wang, Yanzhao, Gao, Qingkun, Zeng, Qingmin, Niu, Pengfei, Li, Cheng, Gu, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2019
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.
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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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