基于深度学习的儿童上腹部放疗危及器官/结构自动勾画
Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
AI总结：
本研究针对儿童上腹部放疗危及器官勾画需求，开发基于CT的多器官分割模型，通过内部与公开组合数据集训练提升鲁棒性，经量化指标与临床评估验证了良好性能与可用性。
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，用于勾画儿童上腹部肿瘤的危及器官（OARs），并评估其在多个数据集上的鲁棒性。材料与方法：研究使用了来自肾肿瘤和神经母细胞瘤患儿的内部术后CT数据（n=189），以及一个涵盖胸腹部区域CT的公开数据集（n=189）。共勾画了17个危及器官：其中9个由临床医生勾画（1类），8个使用TotalSegmentator勾画（2类）。分别使用内部数据集训练了自动分割模型Model-PMC-UMCU，使用公开数据的组合数据集训练了Model-Combined。采用戴斯相似系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（MSD）评估模型性能。两名临床医生采用5点李克特量表对15例患者的勾画结果进行临床可接受性评分。模型鲁棒性则从性别、年龄、静脉造影剂和肿瘤类型四个维度进行评估。结果：Model-PMC-UMCU在9个1类危及器官中有5个的平均DSC值高于0.95，脾脏和心脏的DSC值在0.90至0.95之间，胃肠和胰腺的DSC值低于0.90。Model-Combined在两个数据集上均表现出更强的鲁棒性。临床评估显示模型可用性良好，两名临床医生对9个1类危及器官中的6个评分高于4分，对8个2类危及器官中的6个评分高于3分。仅在两个数据集的年龄组间发现显著的性能差异，具体体现在左肺和胰腺上，其中0-2岁年龄组的性能最低。结论：本研究开发了一种多器官分割模型，经组合数据集训练后鲁棒性得到提升。该模型适用于多种危及器官的勾画，可应用于临床场景中的多个数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
