Deep learning-based delineation of whole-body organs at risk empowering adaptive radiotherapy.

ABSTRACT:

Background

Accurate delineation of organs at risk (OARs) is crucial for precision radiotherapy. Most previous autosegmentation models were only constructed for single anatomical region without evaluation of dosimetric impact. We aimed to validate the clinical practicability of deep-learning (DL) models for autosegmentation of whole-body OARs with respect to delineation accuracy, clinical acceptance and dosimetric impact.

Methods

OARs in various anatomical regions, including the head and neck, thorax, abdomen, and pelvis, were automatedly delineated by DL models (DLD) and compared to manual delineations (MD) by an experienced radiation oncologist (RO). The geometric performance was evaluated using the Dice similarity coefficient (DSC) and average surface distance (ASD). RO A

SUBMITTER: Chen ZH 

PROVIDER: S-EPMC12265227 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature