Evidence map›Paper›PMID 30453970›Full record

ArticleJournal of biomedical science2018

Personalized risk assessment for dynamic transition of gastric neoplasms.

Jean Ching-Yuan Fann, Tsung-Hsien Chiang, Amy Ming-Fang Yen, Yi-Chia Lee, Ming-Shiang Wu, Hsiu-Hsi Chen

Abstract read
In one paragraph

Article in Journal of biomedical science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. FOLR2Journal of experimental & clinical cancer research : CR · 2024
    Article
  5. Article
  6. Assessment ofHeliyon · 2024
    Article
  7. Review
  8. Review
  9. Helicobacter pylori infection.Nature reviews. Disease primers · 2023
    Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jean Ching-Yuan FannDepartment of Health Industry Management, School of Healthcare Management, Kainan University, Taoyuan City, Taiwan.
Tsung-Hsien ChiangDepartment of Internal Medicine, College of Medicine, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan.
Amy Ming-Fang YenSchool of Oral Hygiene, College of Oral Medicine, Taipei Medical University, No. 250, Wu-Hsing Street, Xinyi District, Taipei, 110, Taiwan. amyyen@tmu.edu.tw.
Yi-Chia LeeDepartment of Internal Medicine, College of Medicine, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan. yichialee@ntu.edu.tw.ORCID http://orcid.org/0000-0002-8160-1216
Ming-Shiang WuDepartment of Internal Medicine, College of Medicine, National Taiwan University Hospital, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan.
Hsiu-Hsi ChenInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

Funding

Ministry of Education NTU-107L9003Ministry of Science and Technology, Taiwan MOST 107-3017-F-002-003National Taiwan University Hospital 107-T11
6 · The paper itself

Abstract

backgroundTo develop an individually-tailored dynamic risk assessment model following a multistep, multifactorial process of the Correa's gastric cancer model.

methodsFirst, 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.

resultsThe 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%.

conclusionsWe 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.

Indexed as

EnvironmentEpigenesis, GeneticAdultAgedCarcinogenesisCohort StudiesDisease ProgressionEnvironmental BiomarkersHumansIncidenceMiddle AgedModels, TheoreticalRisk AssessmentRisk FactorsStomach NeoplasmsTaiwanEnvironmental BiomarkersEndoscopyGastric cancerHelicobacter pyloriPrevention

Identifiers

PMID30453970
PMCPMC6245882

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.