ArticlePhysics and imaging in radiation oncology2025
Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and purpose: Deep-learning auto-segmentation (DLAS) performance in radiotherapy may change over time due to data shift/drift or practice changes, yet guidance for quality assurance is lacking. This study developed a practical framework for prospective performance monitoring using retrospective data. Methods: A total of 464 prostate cases over 20 months were retrospectively collected. Two commercial DLAS models were clinically used: model A (2D U-Net, January 2022-January 2023) and model B (3D U-Net, February-August 2023). The agreement between DLAS and clinical contours was assessed using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Surface DSC with a 2 mm tolerance (SDSC). Statistical process control charts were created to monitor performance drift and model switching. The first 150 cases were used to define organ-specific control limits with two and three standard deviations of monthly mean values, Results: 2 Conclusion: The monitoring system effectively detected out-of-distribution outliers and clinical practice changes, providing a reliable framework for early detection of monthly performance degradation.
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