Cardiovascular diseases (CVDs) caused 17.7 million deaths in 2015, accounting for one third of all deaths globally. Amongst 8.8 million cancer-deaths in 2015, lung cancer contributed to 1.69 million deaths worldwide. In the United States, lung cancer will claim 154,050 lives in 2018, representing a quarter of all cancer deaths. Both CVDs and lung cancer share several risk factors, including unhealthy dietary patterns, obesity, and tobacco use. Low-dose Computed Tomography (LDCT), an effective lung cancer screening technique, reduces the cancer-related mortality rate by 20% in those with history of heavy smoking. Such subjects have substantially higher risk of CVDs than lung cancer. Indeed, most subjects undergoing lung cancer screening with LDCT have an intermediate to high risk for CVDs. Concomitant CVD screening will have profound benefits for these subjects at no additional time, cost, or radiation dose to the patient. The goal of the project is to develop an automated workflow of cardiovascular morbidity and mortality risk (CARMOR) evaluation with lung cancer screening LDCT data to provide high-risk subjects a “radiation-free” CVD screening without cost or time constraints. Our central hypothesis is that deep radiomics from LDCT follow-up scans and across different reconstructions can effectively improve the performance of CARMOR estimation. To validate our hypothesis, we will perform radiomics analysis on CARMOR with lung cancer screening LDCT scans, and then further enhance the CARMOR features with complementary image reconstructions from the same scan and serial LDCT scans for optimized prognostic performance. The CARMOR quantification methods in lung screening LDCT will be validated retrospectively against multiple subspecialty radiologists with Coronary Computed Tomography Angiogram (CCTA) as the standard of reference. The project requires us to perform analyses over serial LDCT exams and different reconstruction techniques and settings. Therefore, major innovations and improvements need to be made for this challenging deep radiomic analysis task. Since the risk factors of CVDs and lung cancer are closely related with lifestyle, the findings of the project can also be used to create teachable moments for patients to change towards healthier lifestyles. In context of the Center for Medicare & Medicaid Services (CMS) reimbursement of LDCT for lung cancer screening and stress on the importance of considering co-morbidities, our timely project will have a significant impact on the healthcare of a large population. At the end of the project, we will have developed an automated workflow of assessing CARMOR for lung screening LDCT with zero radiation added. We will also have acquired study results to enable personalized patient education to address the long-term health crisis caused by unhealthy lifestyles. Our studies will lead to new understanding of deep radiomics in CVD screening and the impact of LDCT reconstructions in clinical practice.

Low-dose Computed Tomography (LDCT) is effective in lung cancer screening and can reduce the mortality rate by 20% for the high-risk population. However, heavy smokers are affected by cardiovascular diseases (CVDs) even more than by lung cancer and most high-risk subjects undergoing lung cancer screening with LDCT have an intermediate to high risk for CVDs. The goal of the project is to develop an automated workflow to provide high-risk subjects a “radiation-free” CVD screening using lung cancer screening LDCT data.