Optimizing Lung Cancer Screening Nodule Evaluation

优化肺癌筛查结节评估

• 批准号: 10317717
• 负责人: Chung Yin Kong
• 金额: $ 75.19万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10317717
• 关键词: Age; Algorithms; American; Biopsy; British; Cancer Control; Cancer Etiology; Cancer Intervention and Surveillance Modeling Network; Cancer Model; Cessation of life; Characteristics; Chest; Clinical Data; Clinical Trials; Cost Effectiveness Analysis; Costs and Benefits; Enrollment; Equilibrium; Ethnic group; Evaluation; Fibrinogen; Goals; Growth; Guidelines; Image; Impact evaluation; Life Expectancy; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Minority; Minority Groups; Modeling; Natural History; Nodule; Outcome; Participant; Patients; Pattern; Performance; Policies; Population; Population Heterogeneity; Procedures; Protocols documentation; Quality of life; Quality-Adjusted Life Years; Race; Registries; Risk; Risk Behaviors; Sampling; Screening for cancer; Smoking; Smoking History; Societies; Structure; Techniques; Testing; Woman; base; burden of illness; cancer risk; clinically significant; comorbidity; comparative cost effectiveness; comparative effectiveness; computed tomography screening; cost; cost effectiveness; diagnostic accuracy; ethnic diversity; follow-up; improved; innovation; low dose computed tomography; lung cancer screening; men; models and simulation; mortality; mortality risk; multi-racial; novel; personalized approach; personalized management; programs; racial and ethnic; racial diversity; screening; screening guidelines; sex; tool

SUMMARY The goal of this project is to optimize the management of screen-detected pulmonary nodules thus maximizing the benefits of lung cancer screening. Lung cancer is the most common cause of cancer death in the US. To curb the burden of this disease, multiple national organizations recommend lung cancer screening with low- dose computed tomography (LDCT). However, 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. The project Specific Aims are to: 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.

概括 该项目的目标是优化筛查检测到的肺结节的管理，从而最大限度地提高 肺癌筛查的好处。肺癌是美国最常见的癌症死亡原因。到 为了遏制这种疾病的负担，多个国家组织建议采用低水平的肺癌筛查 剂量计算机断层扫描（LDCT）。然而，高达三分之一的 LDCT 筛查可识别肺部结节 但其中只有 1-3% 是癌症。然后对屏幕检测到的肺结节进行额外的随访 影像学检查，在某些情况下，还有侵入性和潜在有害的程序。后续及后续 检查程序占筛查相关不必要伤害和成本的很大一部分。一个 最佳的结节管理算法应该大大减少这些危害并提供早期癌症治疗 检测的好处。然而，肺癌期间检测到的肺结节的最佳治疗方法 筛查目前未知。对于 LDCT 筛查检测到的肺结节有不同的主要指南 管理。最广泛实施的指南侧重于结节特征来决定是否需要和 跟进类型。这些指南未能纳入其他关键患者因素，如年龄、性别、吸烟情况 病史和合并症。此外，其他因素也会严重影响诊断的准确性和 结节管理策略的危害以及最终肺癌筛查的好处。这些包括： 1) 基于参与者和结节特征的肺癌风险； 2）癌症侵袭性； 3）类型， 结节随访的顺序和时间； 4）随访及活检相关并发症； 5）竞争风险 死亡（非肺癌死亡率）； 6) 评估对生活质量的影响。此外，差异 吸烟模式、肺癌风险以及不同种族和族裔群体之间的合并症并不重要 纳入当前的结核管理指南。在这个项目中，我们将使用仿真建模来 有效地确定考虑上面列出的所有问题的最佳算法。我们将建立一个模拟 模型，多种族和民族肺癌模型 (MELCAM)，基于先前的建模框架 我们的团队使用它来广泛研究肺癌控制的各个方面。该项目的具体目标是： 1) 推导并验证 MELCAM 以模拟筛查的管理和后续结果 来自不同种族和民族背景的参与者； 2）使用MELCAM比较现有结节 总体和质量调整后的生命年收益和危害方面的管理协议； 3）使用MELCAM 生成考虑结节和患者因素对结节的影响的结节管理算法 癌症风险、筛查危害和预期寿命，以优化后续手术的类型和时间安排； 4) 确定现有和新颖的后续算法的成本效益。我们的研究具有创新性 应用最先进的建模技术和个性化方法来优化肺部 结节管理最大限度地提高不同人群肺癌筛查的益处。