Study
NLST
(Learn more about this study)
Project ID
NLST-838
Initial CDAS Request Approval
Sep 24, 2021
Title
Optimizing Lung Cancer Screening Nodule Evaluation
Summary
The goal of this project is to optimize the management of screen-detected pulmonary nodules thus maximizing the benefits of lung cancer screening. Up to one third of screening LDCTs identify pulmonary nodules but only 1-3% of these are cancers. Screen-detected pulmonary nodules are then followed-up with additional imaging tests and, in some cases, invasive and potentially harmful procedures. Follow-up and subsequent work-up procedures account for a large portion of screening-associated unnecessary harms and costs. An optimal nodule management algorithm should substantially reduce these harms and provide early cancer detection benefits. However, the optimal management of pulmonary nodules detected during lung cancer screening is currently unknown. There are differing major guidelines for LDCT screen-detected lung nodule management. Most widely implemented guidelines focus on nodule characteristics to decide the need for and type of follow-up. These guidelines fail to incorporate other key patient factors such as age, sex, smoking history, and comorbidities. Furthermore, additional factors can heavily impact the diagnostic accuracy and harms of nodule management strategies and ultimately, the benefits of lung cancer screening. These include: 1) risk of lung cancer based on participant and nodule characteristics; 2) cancer aggressiveness; 3) type, sequence and timing of nodule follow-up; 4) follow-up and biopsy related complications; 5) competing risks of death (non-lung cancer mortality); and 6) impact of evaluation on quality of life. Furthermore, differences in smoking patterns, lung cancer risk, and comorbidities among diverse race and ethnic groups are not incorporated in current nodule management guidelines. In this project, we will use simulation modeling to efficiently determine optimal algorithms that consider all the issues listed above. We will build a simulation model, the Multi-Racial and Ethnic Lung Cancer Model (MELCAM), based on a previous modeling framework used by our team to extensively study various aspects of lung cancer control.
Aims
1) Derive and validate MELCAM to simulate the management and subsequent outcomes of screening participants from diverse racial and ethnic backgrounds;
2) Use MELCAM to compare existing nodule management protocols in terms of overall and quality-adjusted life-year gains and harms;
3) Use MELCAM to generate nodule management algorithm(s) that consider the impact of both nodule and patient factors on cancer risk, screening harms, and life expectancy to optimize the types and timing of follow-up procedures; and
4) Determine the cost-effectiveness of existing and novel follow-up algorithms. Our study is innovative in applying state-of-the-art modeling techniques and personalized approaches to the optimization of pulmonary nodule management maximizing the benefits of lung cancer screening in diverse populations.
Collaborators
Chun Yin Kong, Icahn School of Medicine at Mount Sinai
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