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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

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.

总结