Clinically validated deep learning auto-contouring for glioblastoma volumetric modulated arc therapy planning: a dosimetric equivalence study

Abstract

PURPOSE: Manual delineation of target volumes and organs-at-risk (OARs) for glioblastoma radiotherapy is a critical bottleneck prone to inter-observer variability. This study developed a fully automated, dual-modality deep learning framework to validate its clinical utility by demonstrating the dosimetric equivalence of generated contours against an expert standard. MATERIALS AND METHODS: A deep learning framework was developed using a retrospective dataset of 100 patients. It integrates two specialized networks operating without pre-alignment: a specialized dual-encoder attention U-Net for computed tomography (CT) (used for OARs delineation and dose calculation), and a single-encoder attention U-Net for T2-FLAIR magnetic resonance imaging (MRI) (used for precise target volumes definition). A modality dispatcher routes images to the appropriate model. Geometric performance was evaluated using the Dice similarity coefficient (DSC). For clinical validation, automated CT contours were used to generate volumetric modulated arc therapy plans. These plans were compared to those based on expert manual contours via paired statistical analysis. RESULTS: The framework demonstrated excellent geometric accuracy on the independent test set. Mean planning target volume (PTV) DSC was 0.94 ± 0.03 on MRI and 0.92 ± 0.04 on CT. Dosimetric analysis confirmed clinical viability. No statistically significant differences (p > 0.05) were observed between automated and manual plans for PTV coverage (D₉₅%), OAR maximum doses, or any evaluated metric. CONCLUSION: The automated framework provides accurate segmentation on both CT and MRI. Demonstrated dosimetric equivalence validates clinical reliability and potential to enhance planning efficiency while reducing inter-observer variability.