Optimizing Personalized Screening and Diagnostic Decisions for Lung Cancer Based on Dynamic Risk Assessment and Life Expectancy

基于动态风险评估和预期寿命优化肺癌的个性化筛查和诊断决策

• 批准号: 10419033
• 负责人: Iakovos Toumazis
• 金额: $ 66.89万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10419033
• 关键词: Address; Advisory Committees; Age; Age-Years; Anxiety; Benign; Biopsy; Blood; Cancer Etiology; Cancer Patient; Cessation of life; Data; Data Management Resources; Decision Making; Decision Modeling; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Dose; Early Diagnosis; Effectiveness; Eligibility Determination; Ensure; Family; Guidelines; Health Benefit; Histology; Individual; Intervention; Knowledge; Lead; Life Expectancy; Lung; Lung CAT Scan; Lung diseases; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Modality; Modeling; Morbidity - disease rate; Natural History; Nodule; PET/CT scan; Patients; Population; Population Group; Preventive service; Process; Race; Radiation exposure; Randomized Clinical Trials; Recommendation; Recording of previous events; Reporting; Research; Resources; Risk; Risk Assessment; Risk Factors; Schedule; Secondary Cancer Prevention; Smoke; Smoker; Smoking; Smoking History; Testing; Time; United States; Update; base; blood-based biomarker; cancer diagnosis; cancer risk; compare effectiveness; cost; cost effective; cost effectiveness; cost-effectiveness evaluation; diagnostic biomarker; diagnostic strategy; effectiveness evaluation; emotional distress; high risk; improved; low dose computed tomography; lung cancer prevention; lung cancer screening; mortality; mortality risk; non-smoking; personalized diagnostics; personalized management; personalized screening; programs; psychological distress; risk prediction model; screening; screening guidelines; screening program; sex; smoking cessation; smoking exposure; standardize guidelines

Project Summary/Abstract Lung cancer is the leading cause of cancer related deaths in the United States and worldwide. Most patients are diagnosed with advanced stage disease for which available treatment interventions offer minimal survival benefit. Early detection through screening is vital to achieve cure and minimize lung cancer morbidity and mortality. Low- dose computed tomography (LDCT) has become the standard lung cancer screening modality based on data from randomized clinical trials. In 2021, the US Preventive Services Task Force (USPSTF) relaxed its lung cancer screening eligibility criteria (based on age and smoking history) providing coverage to younger and lighter smokers. Even though the eligibility expansion is expected to enhance benefits in specific population groups, many newly eligible individuals would have low lung cancer risk making it less likely to benefit from screening, but will be subject to potential harms such as false-positive findings and risks from invasive diagnostic procedures, emotional and psychological distress, and cost. Thus, it is imperative to accurately identify individuals that are likely to benefit from screening. Management of indeterminate findings is challenging, given the high rates of benign nodules detected by LDCT. Existing lung cancer screening and diagnostic guidelines ignore important risk-factors, whereas promising risk prediction models assessing screening eligibility of individuals and malignancy of indeterminate findings omit life-expectancy and remain underutilized. This research aims to develop individualized, dynamic risk-based screening and diagnostic strategies through stochastic, dynamic decision models. This project leverages the individualizEd luNG cAncer screeninG dEcisions (ENGAGE) framework – a previously developed and validated framework – that offers individualized screening decisions by dynamically assessing the risk and life expectancy of ever-smoked individuals. We will expand the current version of ENGAGE, which is based on age, sex, and smoking history, to incorporate non-smoking risk factors including race, family history and history of pulmonary disease among others, into the decision-making process. We will develop microsimulation models to simulate the progression of pulmonary nodules and overlay a partially observable Markov decision process to optimize the diagnostic management of pulmonary nodules at the patient level, based on a risk assessment for the nodule’s malignancy and information collected from serial LDCT, biopsy, PET/CT or a diagnostic blood-based biomarker. We will integrate the diagnostic module into ENGAGE to derive state-of-the-art individualized screening and diagnostic recommendations, and compare the effectiveness, efficiency, and cost-effectiveness of the updated ENGAGE framework against current practice. This project presents a new direction in lung cancer screening research paving the road towards individualized secondary cancer prevention. The expansion of the ENGAGE framework to facilitate a personalized risk-based program that integrates smoking and non-smoking risk factors, along with life expectancy, will form the basis for the development of optimal, cost-effective lung cancer screening guidelines tailored to individuals.

项目摘要/摘要 肺癌是美国和世界范围内与癌症相关的死亡的主要原因。大多数患者都是 被诊断为晚期疾病，现有的治疗干预措施对其生存益处微乎其微。 通过筛查及早发现对于治愈和最大限度地减少肺癌发病率和死亡率至关重要。低- 剂量计算机断层扫描(LDCT)已成为基于数据的标准肺癌筛查方式 来自随机临床试验。2021年，美国预防服务工作组(USPSTF)放松了肺部 癌症筛查资格标准(基于年龄和吸烟史)覆盖较年轻和较轻的人群 吸烟者。尽管扩大资格预计会增加特定人口群体的福利， 许多新符合条件的人患肺癌的风险较低，因此不太可能从筛查中受益， 但会受到诸如假阳性结果和侵入性诊断风险等潜在危害的影响 程序，情绪和心理痛苦，以及费用。因此，必须准确地识别 可能从筛查中受益的个人。考虑到对不确定结果的管理是具有挑战性的 LDCT检出良性结节的比率较高。现行肺癌筛查和诊断指南 忽略重要的风险因素，而有希望的风险预测模型评估筛查资格 不确定发现的个体和恶性肿瘤忽略了预期寿命，仍然没有得到充分利用。 这项研究旨在开发个性化的、动态的基于风险的筛查和诊断策略 随机的、动态的决策模型。该项目利用个性化的肺癌筛查决策 (Engage)框架-之前开发和验证的框架-提供个性化筛选 通过动态评估吸烟者的风险和预期寿命做出决定。我们将扩大 当前版本的Engage基于年龄、性别和吸烟史，以纳入非吸烟风险 将种族、家族史和肺部疾病史等因素纳入决策 进程。我们将开发微观模拟模型来模拟肺结节的进展和覆盖 优化肺结节诊断管理的部分可观测马尔可夫决策过程 患者级别，基于对结节恶性程度的风险评估和从系列收集的信息 LDCT、活检、PET/CT或以血液为基础的诊断生物标志物。我们将把诊断模块集成到 参与得出最先进的个性化筛查和诊断建议，并比较 与现行做法相比，更新的接洽框架的效力、效率和成本效益。 这一项目为肺癌筛查研究提供了一个新的方向，为个体化铺平了道路。 二级癌症预防。扩展接洽框架，以促进基于风险的个性化 整合吸烟和不吸烟风险因素以及预期寿命的计划将构成 为个人量身定做最优、成本效益高的肺癌筛查指南。