Grant Number:	1U01CA319217-01 Interpret this number
Primary Investigator:	Tam, Jamie
Organization:	Rutgers Biomedical And Health Sciences
Project Title:	Optimizing Lung Cancer Interventions in a Dynamic Landscape for Tobacco Use, Early Detection, Treatment, and Survivorship Care
Fiscal Year:	2026

PROJECT SUMMARY / ABSTRACT Advances in prevention efforts, most notably tobacco control interventions and the introduction of low-dose computed tomography, have reduced the death toll of lung cancer. Yet lung cancer continues to be the nation’s leading cancer killer, accounting for 125,000 deaths annually. The landscape for lung cancer prevention and control is evolving, and recent developments could accelerate or hinder progress. New products like e- cigarettes and oral nicotine pouches are altering smoking patterns and, in turn, lung cancer risk, but existing tools to inform lung cancer control do not account for this. Despite the proven efficacy of lung cancer screening, under 20% of eligible individuals are accessing it, highlighting serious implementation gaps. Importantly, many individuals diagnosed with lung cancer are not even eligible for screening, meaning that current screening criteria miss key at-risk individuals. Technologies, including blood-based biomarkers and artificial intelligence, could further improve early detection efforts, but there is insufficient guidance on their effective application. As lung cancer screening and treatment continue to improve, the number of lung cancer survivors will grow—a group that now faces the lifelong threat of cancer recurrence and secondary malignancies; post-treatment cancer prevention must be enhanced to support these survivors. Finally, cancer control needs vary widely across states, yet policymakers lack models tailored to their state's unique healthcare, resources, and tobacco policy contexts. To address these issues, modeling research must translate findings from clinical trials and short-term studies for real-world settings and identify intervention strategies that offer maximal health benefit. For over 20 years, the Cancer Intervention and Surveillance Modeling Network Lung Working Group has evaluated lung cancer prevention strategies and directly contributed to the development and implementation of policies and interventions that reduce the lung cancer burden in the US. We use comparative modeling to evaluate the benefits and harms of different lung cancer screening strategies, and our results have served as the basis for national screening guidelines. Our models quantify the toll of smoking on lung cancer and overall mortality, and assess the impacts of US tobacco control policies. To drive further progress, we propose to: 1) Advance lung cancer prevention with next-generation modeling that reflects the changing tobacco marketplace; 2) Guide strategies that broaden the reach and impact of lung cancer early detection; 3) Optimize lung cancer survivorship care from surveillance to post- treatment risk reduction; and 4) Establish modeling infrastructure to enhance policy planning for lung cancer prevention and control across US states. With a suite of six lung cancer natural history models and a proven record of clinical and policy impact, we aim to continue our trajectory of confronting emerging challenges through leading-edge modeling that informs decision-making across the lung cancer control continuum.





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