Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers.

ABSTRACT:

Purpose

We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical radiotherapy (RT) planning workflow and report on user experience.

Methods and materials

DC models were implemented at two cancer centers and used to generate OAR and CTVs for all patients undergoing RT for a central nervous system (CNS), head and neck (H&N), or prostate cancer. Radiation Therapists/Dosimetrists and Radiation Oncologists completed post-contouring surveys rating the degree of edits required for DCs (1 = minimal, 5 = significant) and overall DC satisfaction (1 = poor, 5 = high). Unedited DCs were compared to the edite

SUBMITTER: Wong J 

PROVIDER: S-EPMC8186196 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature