Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.

ABSTRACT:

Background

At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality.

Methods

A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D compute

SUBMITTER: Cai W 

PROVIDER: S-EPMC10956235 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature