An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.

ABSTRACT:

Purpose

To develop and evaluate an integrated solution for automatic intensity-modulated radiation therapy (IMRT) planning in non-small-cell lung cancer (NSCLC) cases.

Methods

A novel algorithm named as multi-objectives adjustment policy network (MOAPN) was proposed and trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS), based on the multi-agent deep reinforcement learning (DRL) scheme. Furthermore, a three-dimensional (3D) dose prediction module was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOAPN training. 114 previously treated NSCLC cases suitable for stereotactic body radiotherapy (SBRT) were selected from the clinical databas

SUBMITTER: Wang H 

PROVIDER: S-EPMC9936236 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature