Current lung cancer screening eligibility guidelines were developed in a civilian population and miss the majority of Veterans who develop lung cancer. The guidelines include 50-80 year old heavy smokers, with a 20 or more pack years history, who either currently smoke or quit within the last 15 years. These criteria only capture 20-35% of lung cancers in the civilian population and Veterans. Furthermore, Veterans suffer from lung cancer at higher rates than the rest of the United States population, smoke more, and have unique exposures to known causes of lung cancer including Agent Orange, asbestos, diesel fumes, ionizing radiation and Open Burn Pit hydrocarbons. Veterans also have additional risk factors for lung cancer such as race, low socio-economic status, previous history of cancer, HIV, rheumatoid arthritis and chronic obstructive pulmonary disease (COPD) each of which have been shown to increase lung cancer risk. Other, population specific models effectively identify at risk subgroups who may benefit from screening, but none of these models have been validated in Veterans and none consider Veterans’ unique risks. A personalized and Veteran-specific model that adds service-related lung cancer risks and leads to the identification of high-risk groups that may benefit from lung cancer screening is needed. The objective of this proposal is to combine general population and Veteran-specific lung cancer risk factors into a Veteran's lung cancer screening eligibility model. Our overall hypothesis is that service histories and novel risk factors can be used in a Veteran-specific lung cancer risk model to broaden the population who may benefit from lung cancer screening. This effort to improve Veterans’ health through the early detection of lung cancer with screening has two aims. In Aim 1 we will define and discover novel phenotypes associated with increased lung cancer risk in Veterans that include longitudinal clinical and military service-specific exposures. We will generate a comprehensive, longitudinal set of lung cancer risk factors from all Veterans who have received care at a VA facility in the last decade. We will use linked Department of Defense service and VA Electronic Health Record (EHR) data to identify service-related exposures and lung cancer risk factors. Using artificial intelligence, we will mine unstructured text data from clinical notes radiological reports to discover novel data pattern (phenotypes) that help predict future lung cancer diagnosis. We hypothesize that we will accurately determine risk variables used in current eligibility models and discover a set of novel Veteran-specific phenotypes associated with lung cancer risk. In Aim 2 we will build a Veteran-specific lung cancer screening model and compare it to existing screening eligibility criteria and models. We will use a combination of standard lung cancer risk variables, military service-specific risk factors and novel discovered EHR lung cancer risk phenotypes to develop a lung cancer screening model. The variables for this model will include a rich mosaic of static and time varying metrics (smoking behavior, lab values, pulmonary function, etc.), lung cancer risk EHR phenotypes (COPD, HIV, etc.), and service-specific risks (Agent Orange, asbestos, etc.). We will compare our new model to the existing lung cancer screening guidelines, the Bach, Liverpool Lung Project and PLCO screening eligibility models. We hypothesize that a Veteran-specific model will identify more at-risk individuals with greater accuracy and calibration compared to current screening eligibility models. With nationally recognized leaders in lung cancer, informatics, VA data use, machine learning, epidemiology, and biostatistics, we are uniquely positioned to accomplish these goals. At the completion of this proposal, a Veteran-specific model will be developed and compared to existing lung cancer screening eligibility models for at-risk Veterans.

Current lung cancer screening eligibility guidelines use only age and smoking history, were developed in a civilian population and miss the majority of Veterans who develop lung cancer. Veterans suffer from lung cancer at higher rates than the rest of the United States population and have unique exposures due to their military service. This proposal will define Veteran-specific lung cancer risk factors, develop a screening eligibility model for Veterans and compare that model to current eligibility risk models and criteria. We will use linked Department of Defense and VA Electronic Health Records to discover novel phenotypes associated with lung cancer risk. We will then build a Veteran-specific lung cancer screening model using standard, machine and deep learning methods and compare it to existing screening models. With nationally recognized leaders in machine learning, Veteran’s health data, lung cancer screening modeling, and health services research, we are uniquely positioned to use artificial intelligence to improve lung cancer screening for Veterans.