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The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program

BACKGROUND: In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history fac...

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Autores principales: Saya, Sibel, Emery, Jon D, Dowty, James G, McIntosh, Jennifer G, Winship, Ingrid M, Jenkins, Mark A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583148/
https://www.ncbi.nlm.nih.gov/pubmed/33134836
http://dx.doi.org/10.1093/jncics/pkaa062
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author Saya, Sibel
Emery, Jon D
Dowty, James G
McIntosh, Jennifer G
Winship, Ingrid M
Jenkins, Mark A
author_facet Saya, Sibel
Emery, Jon D
Dowty, James G
McIntosh, Jennifer G
Winship, Ingrid M
Jenkins, Mark A
author_sort Saya, Sibel
collection PubMed
description BACKGROUND: In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios. METHODS: We estimated the model’s predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group. RESULTS: Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group. CONCLUSIONS: A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement.
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spelling pubmed-75831482020-10-29 The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program Saya, Sibel Emery, Jon D Dowty, James G McIntosh, Jennifer G Winship, Ingrid M Jenkins, Mark A JNCI Cancer Spectr Article BACKGROUND: In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios. METHODS: We estimated the model’s predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group. RESULTS: Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group. CONCLUSIONS: A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement. Oxford University Press 2020-07-18 /pmc/articles/PMC7583148/ /pubmed/33134836 http://dx.doi.org/10.1093/jncics/pkaa062 Text en © The Author(s) 2020. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Saya, Sibel
Emery, Jon D
Dowty, James G
McIntosh, Jennifer G
Winship, Ingrid M
Jenkins, Mark A
The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program
title The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program
title_full The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program
title_fullStr The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program
title_full_unstemmed The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program
title_short The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program
title_sort impact of a comprehensive risk prediction model for colorectal cancer on a population screening program
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583148/
https://www.ncbi.nlm.nih.gov/pubmed/33134836
http://dx.doi.org/10.1093/jncics/pkaa062
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