Ding Mianyong, Maspero Matteo, Littooij Annemieke S, van Grotel Martine, Fajardo Raquel Davila, van Noesel Max M, van den Heuvel-Eibrink Marry M, Janssens Geert O
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands; Department of Radiation Oncology, Imaging and Cancer Division, University Medical Center Utrecht, Utrecht, The Netherlands; Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Department of Radiation Oncology, Imaging and Cancer Division, University Medical Center Utrecht, Utrecht, The Netherlands; Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Radiother Oncol. 2025 Jul;208:110914. doi: 10.1016/j.radonc.2025.110914. Epub 2025 May 4.
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.
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 (Model-PMC-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.
Model-PMC-UMCU achieved mean DSC values above 0.95 for five of nine OARs, while the 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 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.
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.
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，用于描绘小儿上腹部肿瘤中的危及器官（OARs），并评估其在多个数据集中的稳健性。
使用了来自患有肾肿瘤和神经母细胞瘤的儿科患者的内部术后CT（n = 189）以及一个涵盖胸腹部区域CT的公共数据集（n = 189）。划定了17个OARs：9个由临床医生划定（1型），8个使用TotalSegmentator划定（2型）。使用内部数据（Model-PMC-UMCU）和公共数据的组合数据集（Model-Combined）训练自动分割模型。使用骰子相似系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（MSD）评估性能。两名临床医生对15个患者轮廓在5点李克特量表上对临床可接受性进行评分。针对性别、年龄、静脉造影剂和肿瘤类型评估模型的稳健性。
Model-PMC-UMCU在9个OARs中的5个上实现了平均DSC值高于0.95，而脾脏和心脏的DSC值在0.90至0.95之间。胃-肠和胰腺的DSC值低于0.90。Model-Combined在两个数据集中均表现出更高的稳健性。临床评估显示可用性良好，两名临床医生对9个1型OARs中的6个评分高于4分，对8个2型OARs中的6个评分高于3分。仅在两个数据集中的不同年龄组之间发现了显著的性能差异，特别是在左肺和胰腺中。0至2岁年龄组的性能最低。
开发了一种多器官分割模型，在组合数据集上训练时显示出更高的稳健性。该模型适用于各种OARs，可应用于临床环境中的多个数据集。
