标题： 基于深度学习的儿童上腹部放疗器官/危及结构自动勾画 显示英文标题

标题： Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy

摘要： 目的：本研究旨在开发一种基于计算机断层扫描（CT）的多器官分割模型，用于描绘儿童上腹部肿瘤的风险器官（OAR），并评估其在多个数据集上的鲁棒性。 材料与方法：使用了来自患有肾肿瘤和神经母细胞瘤的儿童患者的内部术后CT图像（n=189）以及一个公开数据集（n=189），该数据集包含覆盖胸腹区域的CT图像。总共勾画了17个OAR：其中9个由临床医生勾画（类型1），另外8个使用TotalSegmentator勾画（类型2）。自动分割模型分别用内部数据集（ModelPMC-UMCU）和包含公共数据的组合数据集（Model-Combined）进行了训练。性能通过Dice相似性系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（MSD）来评估。两名临床医生根据Likert量表对15个患者轮廓的临床可接受性进行了评分。模型的鲁棒性针对性别、年龄、静脉对比剂和肿瘤类型进行了评估。 结果： Model-PMC-UMCU对于9个OAR中的5个达到了超过0.95的平均DSC值，而脾脏和心脏的DSC值在0.90到0.95之间。胃肠道和胰腺的DSC值低于0.90。Model-Combined在两个数据集上都表现出更好的鲁棒性。临床评估显示良好的可用性，两位临床医生对9个类型1 OAR中的6个评分高于4分，对8个类型2 OAR中的6个评分高于3分。仅在两个数据集中不同年龄段之间的左肺和胰腺发现了显著的性能差异。0-2岁年龄组表现最差。 结论：开发了一种多器官分割模型，在联合数据集上训练时表现出增强的鲁棒性。该模型适用于各种OAR，并可在临床环境中应用于多个数据集。 显示英文摘要

摘要： Purposes: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets. Materials and methods: In-house postoperative CTs from pediatric patients with renal tumors and neuroblastoma (n=189) and a public dataset (n=189) with CTs covering thoracoabdominal regions were used. Seventeen OARs were delineated: nine by clinicians (Type 1) and eight using TotalSegmentator (Type 2). Auto-segmentation models were trained using in-house (ModelPMC-UMCU) and a combined dataset of public data (Model-Combined). Performance was assessed with Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and mean surface distance (MSD). Two clinicians rated clinical acceptability on a 5-point Likert scale across 15 patient contours. Model robustness was evaluated against sex, age, intravenous contrast, and tumor type. Results: Model-PMC-UMCU achieved mean DSC values above 0.95 for five of nine OARs, while spleen and heart ranged between 0.90 and 0.95. The stomach-bowel and pancreas exhibited DSC values below 0.90. Model-Combined demonstrated improved robustness across both datasets. Clinical evaluation revealed good usability, with both clinicians rating six of nine Type 1 OARs above four and six of eight Type 2 OARs above three. Significant performance 2 differences were only found across age groups in both datasets, specifically in the left lung and pancreas. The 0-2 age group showed the lowest performance. Conclusion: A multi-organ segmentation model was developed, showcasing enhanced robustness when trained on combined datasets. This model is suitable for various OARs and can be applied to multiple datasets in clinical settings.