Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.

ABSTRACT:

Background and purpose

Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased.

Materials and methods

Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO₀). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO₁). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MI_Mean, MPI_Mean), derived from a tr

SUBMITTER: Thor M 

PROVIDER: S-EPMC8552336 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature