Shawn Stapleton

PhD

Philips Research North America

Senior Data Scientist

NLST (Learn more about this study)

NLST-241

Sep 16, 2016

Improved lung cancer screening with cognitive computing

Every year, 200,000 people are diagnosed with lung cancer resulting in 160,000 deaths per year (450 people per day) in the US alone. Early detection can increase the five-year survival rate for stage 1 lung cancer to nearly 90%. Screening patients with low-dose computed tomography (LDCT) has been proven to find lung cancer at its earliest stages ad reduce mortality. As a result, the number of lung cancer screenings by LDCT is expected to increase rapidly.

To address this new paradigm, we plan to leverage rich, high level features with deep learning and context with joint modeling such that clinicians and hospitals can provide optimal lung cancer screening both in terms of patient outcomes and clinical workflow. To achieve this goal we plan to leverage algorithms built from the largescale, multi-site observational NLST dataset. With this rich information, we test the feasibility of improving the differentiation of an individual patient’s risk, assisting clinicians in directing patients to optimal clinical care, and providing that information in an automated fashion to fits seamlessly in clinical workflow.