Optimizing Lung Cancer Screening Nodule Evaluation

优化肺癌筛查结节评估

基本信息

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
项目类别：
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

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

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项目摘要

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.

概括该项目的目的是优化屏幕检测的肺结核的管理，从而最大化肺癌筛查的好处。肺癌是美国癌症死亡的最常见原因。到遏制这种疾病的负担，多个国家组织建议以低 - 剂量计算机断层扫描（LDCT）。但是，多达三分之一的筛选LDCT识别肺结核但是其中只有1-3％是癌症。然后跟踪筛网检测的肺结核成像测试，在某些情况下是侵入性和潜在有害程序。后续和随后锻炼程序是大部分筛查相关的不必要的危害和成本。一个最佳结节管理算法应大大减少这些危害并提供早期癌症检测益处。但是，在肺癌期间检测到的肺结核的最佳管理筛查目前未知。 LDCT屏幕检测的肺结节有不同的主要准则管理。最广泛实施的指南专注于结节特征，以决定和随访类型。这些准则未能纳入其他关键患者因素，例如年龄，性别，吸烟历史和合并症。此外，其他因素可能会严重影响诊断准确性和危害结节管理策略，最终是肺癌筛查的好处。其中包括： 1）基于参与者和结节特征的肺癌风险； 2）癌症的侵略性； 3）类型，结节随访的序列和时机； 4）随访和活检相关并发症； 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，差异不同种族和种族之间的吸烟模式，肺癌风险以及合并症不是并入当前的结节管理指南。在这个项目中，我们将使用仿真建模到有效地确定考虑上述所有问题的最佳算法。我们将构建模拟基于先前的建模框架的模型，多种族和种族肺癌模型（MELCAM）我们的团队用于广泛研究肺癌控制的各个方面。该项目的特定目的是： 1）得出并验证MELCAM以模拟筛选的管理和后续结果来自不同种族和种族背景的参与者； 2）使用MelCAM比较现有的结节在整体和质量调整的生活年度增长和危害方面的管理方案； 3）使用梅尔卡姆产生结节管理算法，该算法考虑结节和患者因素对癌症风险，筛查危害和预期寿命，以优化随访程序的类型和时机； 4）确定现有和新型随访算法的成本效益。我们的研究是创新的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化肺癌筛查在不同人群中的好处。