Consistency evaluation and performance optimization of deep learning-based auto-contouring for nasopharyngeal carcinoma.

ABSTRACT: Contour delineation is crucial for ensuring the efficacy and side effects of radiotherapy (RT), but it inevitably involves inter-observer variability (IOV). Deep learning (DL) models have been used to assist in contour delineation, but further evaluation is needed to guide healthcare professionals in the judicious application of DL models. The contours of 22 anatomical structures and the gross tumor volume (GTV) for 30 patients with nasopharyngeal carcinoma were delineated using four DL models: AccuContour, RT-Viewer-contour, RT-Mind, and PVmed Contouring. The overall kappa values and generalized conformity indices of these contours were calculated to assess consistency. The Dice similarity coefficient (DSC), Relative Volume Difference (RVD), 95th percentile Hausdorff Distance (HD95), and

SUBMITTER: Yan L 

PROVIDER: S-EPMC12848034 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature