Personalized, Dynamic Risk-based Lung Cancer Screening

项目摘要/摘要 肺癌是美国癌症相关死亡的主要原因。大多数患者都是 诊断为晚期疾病，可用的治疗干预措施对其存活率最低。fit. 尽管最近在筛查和治疗方法方面取得了进展，但早期发现对于实现治愈和 加强疾病管理。低剂量计算机断层扫描已成为 全国肺癌筛查试验结束后的肺癌--特异性fic 降低死亡率。然而，关于筛查符合条件的人口，最理想的人数，存在相当大的争论 筛查间隔以及肺癌筛查的开始和停止年龄，导致了 现有建议。此外，低剂量的计算机断层扫描与潜在的危害有关，包括， 假阳性结果、辐射暴露和过度诊断。现行肺癌筛查分层指南 基于年龄和吸烟史的个体，忽略了与肺癌相关的其他重要危险因素 发展。 这项拟议的研究旨在通过开发个性化的、动态的基于风险的筛查来改进肺癌筛查 通过随机、动态决策模型筛选策略。这个项目利用了一个已发表的肺癌 在没有任何干预的情况下模拟疾病进展的自然病史模型，以及肺 癌症特异性fic风险预测模型，用于在个性化水平上估计发生肺癌的风险。我们 将肺癌筛查问题描述为一个离散时间部分可观测马尔可夫模型(fiNite Horizon) 随机条件下肺癌筛查序列优化决策过程(POMDP) 健康进步和不完善的状态信息。POMDP模型的目标是最大化预期的 通过筛查有患肺癌风险的无症状个体而获得的寿命。建议的模型 会将筛查历史和个人罹患肺癌的风险纳入决策 提供最先进的个性化筛选策略的过程。预期的最优筛查政策 将在成本效益分析中进行测试，以检查与肺癌筛查相关的成本 是不是fifi所获得的健康益处才是最好的？ 本项目为肺癌筛查排程研究提供了一个新的方向。重要风险因素 包括年龄、性别、种族/民族、筛查史和癌症家族史等，在fluence中 肺癌筛查的效果。拟议的研究承认他们的重要性ficance和地址 将这些因素的动态演化纳入决策的筛选调度问题 进程。该项目的fi规定将构成制定具有成本效益的指南的基础 个性化、基于风险的肺癌筛查。拟议的分析模型有可能成为 扩展到解决影响肺癌筛查的其他令人烦恼的问题。

Project Summary/Abstract Lung cancer is the leading cause of cancer related deaths in the United States. The majority of patients are diagnosed with advanced stage disease for which available treatment interventions offer minimal survival benefit. Despite recent advancements in screening and treatment methods, early detection is vital to achieve cure and enhance disease management. Low-dose computed tomography has become the standard screening modality for lung cancer after the conclusion of the National Lung Screening Trial which reported 20% lung cancer-specific mortality reduction. However, there is considerable debate over the screen eligible population, the optimal screening interval, and the starting and stopping ages of lung cancer screening, causing discrepancies in the existing recommendations. Moreover, low-dose computed tomography is associated with potential harms including, false-positive results, radiation exposure, and overdiagnosis. Existing guidelines for lung cancer screening stratify individuals based on age and smoking history, ignoring other important risk-factors associated with lung cancer development. The proposed research aims to improve lung cancer screening by developing individualized, dynamic risk-based screening strategies through stochastic, dynamic decision models. This project leverages a published lung cancer natural history model to simulate the disease progression in the absence of any intervention, along with a lung cancer-specific risk prediction model to estimate the risk of developing lung cancer on a personalized level. We will formulate the lung cancer screening problem as a finite horizon, discrete time partially observable Markov decision process (POMDP) to optimize the sequence of lung cancer screening examinations under stochastic health progression and imperfect state information. The objective of the POMDP model is to maximize the expected lifetime gained from screening asymptomatic individuals at risk of developing lung cancer. The proposed model will incorporate screening history along with the personal risk of developing lung cancer into the decision making process providing state-of-the-art individualized screening strategies. The anticipated optimal screening policies will be tested in a cost-effectiveness analysis to examine whether the cost associated with lung cancer screening is justifiable by the health benefits gained. This project presents a new direction in lung cancer screening scheduling research. Important risk factors including age, gender, race/ethnicity, screening history, and family history of cancer, among others, influence the effectiveness of lung cancer screening. The proposed research acknowledges their significance and addresses the screening scheduling problem incorporating the dynamic evolution of these factors into the decision making process. The findings of this project will form the basis for the development of cost-effective guidelines for personalized, risk-based lung cancer screening. The proposed analytical models would have the potential to be extended to address other vexing problems affecting lung cancer screening.