ArticlePloS one2015
Prediction Model for Gastric Cancer Incidence in Korean Population.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Effect of physical activity on incidence and mortality in patients with gastric cancer: evidence from real-world studies.Cancer causes & control : CCC · 2023Pooled it
- Comparative validation and local adaptation of gastric cancer risk assessment models in a large multicenter endoscopic screening cohort in China.BMC medicine · 2026Article
- Development of a Combined Risk Factors Prediction Model for Esophageal Squamous Cell Carcinoma: A Secondary Analysis of the Linxian Nutrition Intervention Trial Cohort.Research square · 2026Article
- Lipid-dominated metabolites mediate the association between low body mass index and esophageal malignancy: a population-based nested case-control study.BMC cancer · 2026Article
- Establishment and validation of a prognostic model based on liquid-liquid phase separation-related genes in gastric cancer.Translational cancer research · 2026Article
- A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations.Cancer research and treatment · 2025Article
- Factors influencing the incidence of early gastric cancer: a bayesian network analysis.BMC gastroenterology · 2025Article
- DKK3 and SERPINB5 as novel serum biomarkers for gastric cancer: facilitating the development of risk prediction models for gastric cancer.Frontiers in oncology · 2025Article
- The burden of gastric cancer attributed to high salt intake and predictions through the year 2042: a cross-national comparative analysis of China, Japan, and South Korea.Frontiers in nutrition · 2025Article
- Application of Machine Learning in the Diagnosis of Early Gastric Cancer Using the Kyoto Classification Score and Clinical Features Collected from Medical Consultations.Bioengineering (Basel, Switzerland) · 2024Article
- Combined DNA Methylation and Gastric Microbiome Marker PredictsGut and liver · 2024Article
- Risk prediction model for gastric cancer within 5 years in healthy Korean adults.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis.World journal of gastrointestinal oncology · 2024Article
- Risk Prediction Models for Gastric Cancer: A Scoping Review.Journal of multidisciplinary healthcare · 2024Article
- Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model.Gastro hep advances · 2024Article
- Prediction algorithm for gastric cancer in a general population: A validation study.Cancer medicine · 2023Article
- Gastric Cancer Risk in Association with Underweight, Overweight, and Obesity: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Development, validation, and evaluation of a risk assessment tool for personalized screening of gastric cancer in Chinese populations.BMC medicine · 2023Article
- Evaluation of polygenic risk score for risk prediction of gastric cancer.World journal of gastrointestinal oncology · 2023Review
- Adherence to the World Cancer Research Fund/American Institute for Cancer Research and Korean Cancer Prevention Guidelines and cancer risk: a prospective cohort study from the Health Examinees-Gem study.Epidemiology and health · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting high risk groups for gastric cancer and motivating these groups to receive regular checkups is required for the early detection of gastric cancer. The aim of this study is was to develop a prediction model for gastric cancer incidence based on a large population-based cohort in Korea.
methodBased on the National Health Insurance Corporation data, we analyzed 10 major risk factors for gastric cancer. The Cox proportional hazards model was used to develop gender specific prediction models for gastric cancer development, and the performance of the developed model in terms of discrimination and calibration was also validated using an independent cohort. Discrimination ability was evaluated using Harrell's C-statistics, and the calibration was evaluated using a calibration plot and slope.
resultsDuring a median of 11.4 years of follow-up, 19,465 (1.4%) and 5,579 (0.7%) newly developed gastric cancer cases were observed among 1,372,424 men and 804,077 women, respectively. The prediction models included age, BMI, family history, meal regularity, salt preference, alcohol consumption, smoking and physical activity for men, and age, BMI, family history, salt preference, alcohol consumption, and smoking for women. This prediction model showed good accuracy and predictability in both the developing and validation cohorts (C-statistics: 0.764 for men, 0.706 for women).
conclusionsIn this study, a prediction model for gastric cancer incidence was developed that displayed a good performance.
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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.