Evaluating clinical acceptability of organ-at-risk segmentation In head & neck cancer using a compendium of open-source 3D convolutional neural networks

Background and PurposeAuto-segmentation of organs at risk (OAR) in cancer patients is essential for enhancing radiotherapy planning efficacy and reducing inter-observer variability. Deep learning auto-segmentation models have shown promise, but their lack of transparency and reproducibility hinders their generalizability and clinical acceptability, limiting their use in clinical settings. Materials and MethodsThis study introduces SCARF (auto-Segmentation Clinical Acceptability & Reproducibility Framework), a comprehensive six-stage reproducible framework designed to benchmark open-source convolutional neural networks for auto-segmentation of 19 essential OARs in head and neck cancer (HNC). ResultsSCARF offers an easily implementable framework for designing and reproducibly benchmarking auto-segmentation tools, along with thorough expert assessment capabilities. Expert assessment labelled 16/19 AI-generated OAR categories as acceptable with minor revisions. Boundary distance metrics, such as 95th Percentile Hausdorff Distance (95HD), were found to be 2x more correlated to Mean Acceptability Rating (MAR) than volumetric overlap metrics (DICE). ConclusionsThe introduction of SCARF, our auto-Segmentation Clinical Acceptability & Reproducibility Framework, represents a significant step forward in systematically assessing the performance of AI models for auto-segmentation in radiation therapy planning. By providing a comprehensive and reproducible framework, SCARF facilitates benchmarking and expert assessment of AI-driven auto-segmentation tools, addressing the need for transparency and reproducibility in this domain. The robust foundation laid by SCARF enables the progression towards the creation of usable AI tools in the field of radiation therapy. Through its emphasis on clinical acceptability and expert assessment, SCARF fosters the integration of AI models into clinical environments, paving the way for more randomised clinical trials to evaluate their real-world impact. O_TEXTBOXHighlightsO_LIOur study highlights the significance of both quantitative and qualitative controls for benchmarking new auto-segmentation systems effectively, promoting a more robust evaluation process of AI tools. C_LIO_LIWe address the lack of baseline models for medical image segmentation benchmarking by presenting SCARF, a comprehensive and reproducible six-stage framework, which serves as a valuable resource for advancing auto-segmentation research and contributing to the foundation of AI tools in radiation therapy planning. C_LIO_LISCARF enables benchmarking of 11 open-source convolutional neural networks (CNN) against 19 essential organs-at-risk (OARs) for radiation therapy in head and neck cancer, fostering transparency and facilitating external validation. C_LIO_LITo accurately assess the performance of auto-segmentation models, we introduce a clinical assessment toolkit based on the open-source QUANNOTATE platform, further promoting the use of external validation tools and expert assessment. C_LIO_LIOur study emphasises the importance of clinical acceptability testing and advocates its integration into developing validated AI tools for radiation therapy planning and beyond, bridging the gap between AI research and clinical practice. C_LI C_TEXTBOX