Sylvia Plevritis

Ph.D.

Stanford University

Professor

NLST (Learn more about this study)

NLST-438

Aug 30, 2018

Personalized Dynamic Risk-based Lung Cancer Screening

This research project aims to improve lung cancer screening by developing individualized, dynamic risk-based screening strategies through stochastic, dynamic decision models. We leverage a published lung cancer natural history model to simulate the disease progression in the absence of any intervention, along with a lung cancer-specific 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 finite 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. Towards this end we will use the NLST data to estimate the life expectancy of lung cancer patients stratified by sex, age, and stage of the disease at the time of detection.