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 肺癌是美国和全世界癌症相关死亡的主要原因。大多数患者是 被诊断出患有晚期疾病，可用的治疗干预措施可提供最小的生存益处。 通过筛查的早期检测对于治愈和最大程度地减少肺癌的发病率和死亡率至关重要。低的- 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，美国预防服务工作队（USPSTF）放松了肺 癌症筛查资格标准（基于年龄和吸烟历史）为年轻和更轻的覆盖范围 吸烟者。即使预计资格扩大将提高特定人口群体的收益，但 许多新符合条件的人的肺癌风险低，从而使筛查受益的可能性较小， 但将受到潜在危害，例如虚假阳性发现和侵入性诊断的风险 程序，情感和心理困扰以及成本。那是必须准确识别的 可能会从筛查中受益的个人。不确定发现的管理是挑战的 LDCT检测到的良性结节的高率。现有的肺癌筛查和诊断指南 忽略重要的风险因素，而承诺评估筛查资格的风险预测模型 个人和不确定发现的恶性肿瘤忽略了预期的生活，并且仍然没有得到充分利用。 这项研究旨在通过通过 随机，动态决策模型。该项目利用个性化的肺癌筛查决策 （参与）框架 - 先前开发和验证的框架 - 提供个性化筛选 通过动态评估不断吸烟的个人的风险和预期寿命来决定。我们将扩展 当前版本的参与者基于年龄，性别和吸烟史，以纳入不吸烟的风险 包括种族，家族史和肺部疾病史的因素，进入决策 过程。我们将开发微仿真模型，以模拟肺结节和覆盖的进展 马尔可夫的部分决策过程，以优化肺结节的诊断管理 患者级别，基于对结节的恶性肿瘤的风险评估和从序列收集的信息 LDCT，活检，PET/CT或基于血液的生物标志物。我们将将诊断模块整合到 参与得出最先进的个性化筛查和诊断建议，并比较 针对当前实践的更新的参与框架的有效性，效率和成本效益。 该项目在肺癌筛查研究中提出了一个新的方向 预防继发性癌症。扩展参与框架以促进基于个性化风险的个性化 整合吸烟和非吸烟风险因素以及预期寿命的计划将构成 根据个人量身定制的最佳，具有成本效益的肺癌筛查指南的制定。