Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans

ABSTRACT: Highlights • Cardiotoxicity is a common complication of lung cancer radiotherapy.• Segmentation of cardiac substructures is time-consuming and challenging.• Deep learning segmentation tools can perform this task in 3D and 4D scans.• Performance is high when assessed geometrically, dosimetrically and clinically.• Auto-segmentation tools may accelerate clinical workflows and enable research.

Background

Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures usin

SUBMITTER: Walls G 

PROVIDER: S-EPMC9356270 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature