Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution.

ABSTRACT:

Background and purpose

Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation.

Materials and methods

Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-hi

SUBMITTER: Turcas A 

PROVIDER: S-EPMC10276287 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature