Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.

ABSTRACT:

Purpose

Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive functions are often not included. This paper introduces a novel automatic segmentation tool specifically designed for the unique challenges posed by pediatric patients undergoing brain RT, as well as its seamless integration into the existing clinical workflow.

Methods and materials

Images of 47 pediatric brain cancer patients aged 1 to 20 years old and 33 two-year-old healthy infants were used to train a vision transformer, UNesT, for the segmentation of five brain OARs. The trained model was then incorporated to clinical workflow

SUBMITTER: Mekki L 

PROVIDER: S-EPMC10930010 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature