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 bene?t. 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-speci?c 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-speci?c 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 ?nite 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 justi?able by the health bene?ts 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, in?uence the effectiveness of lung cancer screening. The proposed research acknowledges their signi?cance and addresses the screening scheduling problem incorporating the dynamic evolution of these factors into the decision making process. The ?ndings 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.
This project focuses on the problem of optimizing lung cancer screening for asymptomatic individuals at risk. The objective of this research is to develop stochastic, dynamic models incorporating past screening exams and the dynamic status of lung cancer risk factors to provide cost-effective, personalized, risk-based screening decisions. The anticipated ?ndings of this proposal will improve the overall effectiveness of screening and enhance the shared decision making process between physicians and patients forming a basis for maximizing health bene?t and reducing the harms associated with lung cancer screening.
