Optimizing Lung Cancer Screening in Cancer Survivors

The goal of the proposal is to identify optimal lung cancer (LC) screening strategies for breast (BC), prostate (PC), colorectal (CRC) cancer survivors. We will accomplish these goals by developing and validating a novel Multi-Racial and Ethnic Lung Cancer Model (MELCAM) that will simulate LC development, progression,

screening, treatment, and survival in a multiethnic cancer survivor population. We will then assess the effectiveness and cost-effectiveness (CE) of different LC screening strategies for these survivors. All together, there are >6 million BC, CRC and PC survivors in the US, and as these cancers tend to be diagnosed in early

stage, many survivors live for a long time and develop and may die from second cancers. Cancer survivors are also at increased risk of developing LC due to relatively high rates of smoking (up to 50-60%), age, and treatment-related side effects. As a result, >15% of LC are diagnosed in cancer survivors, and LC is the top

cause of cancer-related mortality in this population. Little is known about optimal LC screening for cancer survivors who have been excluded from prior randomized trials (RCT) and have a different harm/benefit ratio from screening due to competing risk of death from their first cancer and higher burden of comorbidities. Lack

of data to guide decisions about LC screening in cancer survivors has profound negative impact on survivorship, including under and overuse of LC screening, resulting in worse outcomes and increased healthcare costs. It is unlikely that RCT assessing the benefits of LC screening for cancer survivors will ever be

conducted. Thus, there is an urgent need to use alternative methods to determine the optimal screening strategy for these patients. In this study, we propose using simulation modeling, an approach complementary to RCTs, to assess the harms and benefits of LC screening in cancer survivors. The Specific Aims are to: (1)

Derive and validate a model (MELCAM), based on a well-established framework, to simulate LC screening in BC, PC and CRC cancer survivors from diverse racial and ethnic backgrounds; (2) Determine the most effective and CE strategies for LC screening in BC survivors; (3) Identify the optimal LC screening strategies in

PC survivors and determine their CE; and (4) Evaluate the effectiveness and CE of LC screening for CRC survivors. To achieve these Aims, we will use data from several large, representative, population-based cancer cohorts and robust harmonization methods to develop, calibrate, and validate MELCAM by incorporating the

development, screening, work-up, treatment and survival of LC in multiethnic survivors of BC, PC and CRC (Aim 1). We will then use the model to simulate RCTs evaluating the effectiveness (in terms of maximizing survival, quality of life, and other patient-centered outcomes) and CE of LC screening regimens (eligibility,

frequency and duration) in these cancer survivors (Aims 2-4). The study will be innovative in applying state-of- the-art modeling approaches to evaluate LC screening in a diverse population of cancer survivors, and results will have direct implications for the management of a large group of survivors.