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Evaluating a Risk Prediction Model for Lung Cancer

Screening with low-dose computed tomography (LDCT) presents an opportunity to improve early detection and reduce mortality from lung cancer. However, the potential harms of LDCT screening, including frequent false-positive findings that lead to unnecessary procedures and repeated radiation exposure, have raised concerns. Implementing targeted screening of persons at highest predicted risk for lung cancer may minimize such harms, but empirical evidence to support more personalized risk-based screening remains limited. This study presents a mentored career development plan encompassing training and research activities for establishing independence as a translational cancer epidemiologist, with a primary focus on early detection and management of lung cancer. The goal of the proposed research is to evaluate whether the most predictive and clinically-oriented risk model for lung cancer to date can be validated, extended and applied to aid decision-making about LDCT screening.

Investigator: Sakoda, Lori

Funder: National Cancer Institute

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