IntroductionLeukemia is one of the most prevalent cancers in children. The use of total marrow and lymphoid irradiation (TMLI) via helical tomotherapy (TOMO) as a conditioning regimen prior to bone marrow transplant (BMT) has been widely adopted in clinical practice. Accurate and efficient segmentation of target volumes and organs at risk (OARs) is a prerequisite for precise TMLI. The purpose of this study was to investigate the feasibility of deep learning-based auto-segmentation technology (using 2D U-net and 3D V-net models) for target volumes (bone marrow and lymphatic drainage regions) and organs at risk (OARs) in pediatric total marrow and lymphoid irradiation (TMLI).MethodsThis study was designed as a retrospective study. Thirty-six pediatric patients treated with TMLI between 2018 and 2024 were included. Target volumes and OARs were manually segmented and refined. The CT images and corresponding contours were imported into the AccuLearning workstation (Manteia Company, Xiamen, China) to train, validate, and test based on 2D U-net and 3D V-net deep learning models. The auto-segmentation performance was evaluated on 6 test cases using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Average Surface Distance (ASD).ResultsFinally, analysis revealed DSC values >0.7 for all OARs except lenses segmented by the 3D V-net model. For target volumes, bone structures achieved high segmentation accuracy.ConclusionThe 3D V-net model demonstrated superior performance compared to the 2D U-net model. Auto-segmented contours generated by the 2D U-net and 3D V-net models, with minor manual adjustments, are clinically applicable for TMLI radiotherapy planning.
Xie et al. (Wed,) studied this question.