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

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.