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Personalized risk assessment for dynamic transition of gastric neoplasms

BACKGROUND: To develop an individually-tailored dynamic risk assessment model following a multistep, multifactorial process of the Correa’s gastric cancer model. METHODS: First, we estimated the state-to-state transition rates following Correa’s five-step carcinogenic model and assessed the effect o...

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Autores principales: Fann, Jean Ching-Yuan, Chiang, Tsung-Hsien, Yen, Amy Ming-Fang, Lee, Yi-Chia, Wu, Ming-Shiang, Chen, Hsiu-Hsi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245882/
https://www.ncbi.nlm.nih.gov/pubmed/30453970
http://dx.doi.org/10.1186/s12929-018-0485-6
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author Fann, Jean Ching-Yuan
Chiang, Tsung-Hsien
Yen, Amy Ming-Fang
Lee, Yi-Chia
Wu, Ming-Shiang
Chen, Hsiu-Hsi
author_facet Fann, Jean Ching-Yuan
Chiang, Tsung-Hsien
Yen, Amy Ming-Fang
Lee, Yi-Chia
Wu, Ming-Shiang
Chen, Hsiu-Hsi
author_sort Fann, Jean Ching-Yuan
collection PubMed
description BACKGROUND: To develop an individually-tailored dynamic risk assessment model following a multistep, multifactorial process of the Correa’s gastric cancer model. METHODS: First, we estimated the state-to-state transition rates following Correa’s five-step carcinogenic model and assessed the effect of risk factors, including Helicobacter pylori infection, history of upper gastrointestinal disease, lifestyle, and dietary habits, on the step-by-step transition rates using data from a high-risk population in Matsu Islands, Taiwan. Second, we incorporated information on the gastric cancer carcinogenesis affected by genomic risk factors (including inherited susceptibility and irreversible genomic changes) based on literature to generate a genetic and epigenetic risk assessment model by using a simulated cohort identical to the Matsu population. The combination of conventional and genomic risk factors enables us to develop the personalized transition risk scores and composite scores. RESULTS: The state-by-state transition rates per year were 0.0053, 0.7523, 0.1750, and 0.0121 per year from normal mucosa to chronic active gastritis, chronic active gastritis to atrophic gastritis, atrophic gastritis to intestinal metaplasia, and intestinal metaplasia to gastric cancer, respectively. Compared with the median risk group, the most risky decile had a 5.22-fold risk of developing gastric cancer, and the least risky decile around one-twelfth of the risk. The median 10-year risk for gastric cancer incidence was 0.77%. The median lifetime risk for gastric cancer incidence was 5.43%. By decile, the 10-year risk ranged from 0.06 to 4.04% and the lifetime risk ranged from 0.42 to 21.04%. CONCLUSIONS: We demonstrate how to develop a personalized dynamic risk assessment model with the underpinning of Correa’s cascade to stratify the population according to their risk for progression to gastric cancer. Such a risk assessment model not only facilitates the development of an individually-tailored preventive strategy with treatment for H. pylori infection and endoscopic screening but also provides short-term and long-term indicators to evaluate the program effectiveness.
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spelling pubmed-62458822018-11-26 Personalized risk assessment for dynamic transition of gastric neoplasms Fann, Jean Ching-Yuan Chiang, Tsung-Hsien Yen, Amy Ming-Fang Lee, Yi-Chia Wu, Ming-Shiang Chen, Hsiu-Hsi J Biomed Sci Research BACKGROUND: To develop an individually-tailored dynamic risk assessment model following a multistep, multifactorial process of the Correa’s gastric cancer model. METHODS: First, we estimated the state-to-state transition rates following Correa’s five-step carcinogenic model and assessed the effect of risk factors, including Helicobacter pylori infection, history of upper gastrointestinal disease, lifestyle, and dietary habits, on the step-by-step transition rates using data from a high-risk population in Matsu Islands, Taiwan. Second, we incorporated information on the gastric cancer carcinogenesis affected by genomic risk factors (including inherited susceptibility and irreversible genomic changes) based on literature to generate a genetic and epigenetic risk assessment model by using a simulated cohort identical to the Matsu population. The combination of conventional and genomic risk factors enables us to develop the personalized transition risk scores and composite scores. RESULTS: The state-by-state transition rates per year were 0.0053, 0.7523, 0.1750, and 0.0121 per year from normal mucosa to chronic active gastritis, chronic active gastritis to atrophic gastritis, atrophic gastritis to intestinal metaplasia, and intestinal metaplasia to gastric cancer, respectively. Compared with the median risk group, the most risky decile had a 5.22-fold risk of developing gastric cancer, and the least risky decile around one-twelfth of the risk. The median 10-year risk for gastric cancer incidence was 0.77%. The median lifetime risk for gastric cancer incidence was 5.43%. By decile, the 10-year risk ranged from 0.06 to 4.04% and the lifetime risk ranged from 0.42 to 21.04%. CONCLUSIONS: We demonstrate how to develop a personalized dynamic risk assessment model with the underpinning of Correa’s cascade to stratify the population according to their risk for progression to gastric cancer. Such a risk assessment model not only facilitates the development of an individually-tailored preventive strategy with treatment for H. pylori infection and endoscopic screening but also provides short-term and long-term indicators to evaluate the program effectiveness. BioMed Central 2018-11-19 /pmc/articles/PMC6245882/ /pubmed/30453970 http://dx.doi.org/10.1186/s12929-018-0485-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Fann, Jean Ching-Yuan
Chiang, Tsung-Hsien
Yen, Amy Ming-Fang
Lee, Yi-Chia
Wu, Ming-Shiang
Chen, Hsiu-Hsi
Personalized risk assessment for dynamic transition of gastric neoplasms
title Personalized risk assessment for dynamic transition of gastric neoplasms
title_full Personalized risk assessment for dynamic transition of gastric neoplasms
title_fullStr Personalized risk assessment for dynamic transition of gastric neoplasms
title_full_unstemmed Personalized risk assessment for dynamic transition of gastric neoplasms
title_short Personalized risk assessment for dynamic transition of gastric neoplasms
title_sort personalized risk assessment for dynamic transition of gastric neoplasms
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245882/
https://www.ncbi.nlm.nih.gov/pubmed/30453970
http://dx.doi.org/10.1186/s12929-018-0485-6
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