Reinforcement learning for individualized lung cancer screening schedules: A nested case-control study.

ABSTRACT:

Background

The current guidelines for managing screen-detected pulmonary nodules offer rule-based recommendations for immediate diagnostic work-up or follow-up at intervals of 3, 6, or 12 months. Customized visit plans are lacking.

Purpose

To develop individualized screening schedules using reinforcement learning (RL) and evaluate the effectiveness of RL-based policy models.

Methods

Using a nested case-control design, we retrospectively identified 308 patients with cancer who had positive screening results in at least two screening rounds in the National Lung Screening Trial. We established a control group that included cancer-free patients with nodules, matched (1:1) according to the year of cancer diagnosis. By generating 10,164 sequence decision episodes, we traine

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11215689 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature