Grant Number:	1U01CA319219-01 Interpret this number
Primary Investigator:	Hur, Chin
Organization:	Columbia University Health Sciences
Project Title:	Comparative Modeling of Gastric Cancer Prevention, Screening, and Surveillance in the Us
Fiscal Year:	2026

PROJECT SUMMARY Gastric cancer (GC) remains a major cause of cancer mortality worldwide and a persistent public health challenge in the United States, where it is characterized by heterogeneous population risk, late-stage diagnosis, and high fatality. GC develops through a well-defined precancerous pathway driven by chronic inflammation, primarily due to Helicobacter pylori infection or autoimmune gastritis. Effective interventions exist across both primary prevention (H. pylori eradication) as well as secondary prevention efforts (endoscopic screening and surveillance of precancerous lesions), yet who to target, when to intervene, and how to balance benefits, harms, resource use, and costs remain uncertain in the U.S. due to long disease latency, limited empirical evidence, and rapidly evolving technologies. This U01 renewal will leverage the Gastric Cancer CISNET consortium to conduct comparative, population-based modeling to generate data to inform evidence-based cancer control decisions on GC prevention, screening, and surveillance. Using three independent microsimulation models with harmonized strategies and shared common inputs, we will produce transparent, reproducible projections of long-term outcomes (benefits, harms, healthcare utilization, and cost-effectiveness) and characterize uncertainty within and across models. Aim 1 will evaluate population-based H. pylori test-and-treat strategies for prevention, including integration within routine preventive care, life course timing (including pediatric approaches and reinfection), and antimicrobial resistance tradeoffs. Aim 2 will assess screening strategies for earlier detection, including targeted endoscopic screening, bundled upper endoscopy with colorectal cancer screening, and emerging blood-based biomarkers for early detection. Aim 3 will optimize surveillance for high-risk individuals with precancerous lesions and autoimmune gastritis by evaluating surveillance intervals and stopping ages. Aim 4 will develop scalable, AI-enabled decision-support tools (model emulators and an ensemble interface) to accelerate translation of model-based evidence by synthesizing uncertainty across models and enabling rapid evaluation of guideline- and policy-relevant scenarios. Together, this project will provide rigorous, comparative evidence to guide high-value GC prevention, screening, and surveillance strategies that can reduce GC incidence, improve early detection, and lower GC mortality in the U.S.





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