Optimizing Lung Cancer Screening in Cancer Survivors

该提案的目标是确定乳腺癌（BC）、前列腺癌（PC）和肺癌（LC）的最佳筛查策略。 (PC)结直肠癌（CRC）幸存者。我们将通过开发和验证一种新的 多种族和民族肺癌模型（MELCAM），将模拟LC的发展，进展， 多种族癌症幸存者人群的筛查、治疗和生存。然后我们将评估 有效性和成本效益（CE）的不同LC筛选策略，这些幸存者。所有人一起， 在美国有超过600万BC，CRC和PC幸存者，由于这些癌症往往在早期诊断， 在第二阶段，许多幸存者活了很长时间，并发展成第二种癌症，可能死于第二种癌症。癌症幸存者 由于吸烟率相对较高（高达50-60%）、年龄和 治疗相关的副作用因此，>15%的LC在癌症幸存者中被诊断，并且LC是最高的 癌症相关死亡率的原因。关于癌症的最佳LC筛查知之甚少 从既往随机试验（RCT）中排除且具有不同损害/受益比的幸存者 由于第一次癌症的死亡风险和合并症的负担更高，因此无法进行筛查。缺乏 指导癌症幸存者LC筛查决策的数据对患者的健康产生了深远的负面影响。 生存率，包括LC筛查的不足和过度使用，导致结局更差， 医疗费用。评估癌症幸存者LC筛查益处的RCT不太可能永远 进行。因此，迫切需要使用替代方法来确定最佳筛选 这些患者的策略。在这项研究中，我们提出了使用模拟建模，一种方法互补 随机对照试验，以评估癌症幸存者LC筛查的危害和益处。具体目标是：（1） 基于完善的框架，推导并验证模型（MELCAM），以模拟LC筛选， 来自不同种族和民族背景的BC，PC和CRC癌症幸存者;（2）确定最 有效的和CE策略LC筛查BC幸存者;（3）确定最佳的LC筛查策略， PC幸存者并确定其CE;（4）评价LC筛查CRC的有效性和CE 幸存者为了实现这些目标，我们将使用几个大型的、代表性的、基于人群的癌症数据。 队列和强大的协调方法，以开发，校准和验证MELCAM， 多种族BC、PC和CRC幸存者中LC的发展、筛查、检查、治疗和生存 (Aim 1）。然后，我们将使用该模型来模拟评价有效性的RCT（在最大化 生存率、生活质量和其他以患者为中心的结果）和LC筛选方案的CE（合格性， 频率和持续时间）（目标2-4）。该研究将在应用国家的创新- 最先进的建模方法，以评估LC筛查在不同人群的癌症幸存者，和结果 将对大批幸存者的管理产生直接影响。

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.