Accurate delineation of Gross Tumor Volume (GTV), Lymph Node Clinical Target Volume (LN CTV), and Organ-at-Risk (OAR) from Computed Tomography (CT) scans is essential for precise radiotherapy planning in Nasopharyngeal Carcinoma (NPC). Building upon SegRap2023, which focused on OAR and GTV segmentation using single-center paired non-contrast CT (ncCT) and contrast-enhanced CT (ceCT) scans, the SegRap2025 challenge aims to enhance the generalizability and robustness of segmentation models across imaging centers and modalities. SegRap2025 comprises two tasks: Task01 addresses GTV segmentation using paired CT from the SegRap2023 dataset, with an additional external testing set to evaluate cross-center generalization, and Task02 focuses on LN CTV segmentation using multi-center training data and an unseen external testing set, where each case contains paired CT scans or a single modality, emphasizing both cross-center and cross-modality robustness. This paper presents the challenge setup and provides a comprehensive analysis of the solutions submitted by ten participating teams. For GTV segmentation task, the top-performing models achieved average Dice Similarity Coefficient (DSC) of 74.61% and 56.79% on the internal and external testing cohorts, respectively. For LN CTV segmentation task, the highest average DSC values reached 60.24%, 60.50%, and 57.23% on paired CT, ceCT-only, and ncCT-only subsets, respectively. SegRap2025 establishes a large-scale multi-center, multi-modality benchmark for evaluating the generalization and robustness in radiotherapy target segmentation, providing valuable insights toward clinically applicable automated radiotherapy planning systems. The benchmark is available at: https://hilab-git.github.io/SegRap2025_Challenge.

翻译：从计算机断层扫描（CT）图像中准确勾画大体肿瘤体积（GTV）、淋巴结临床靶区（LN CTV）以及危及器官（OAR），对于鼻咽癌（NPC）的精确放疗计划至关重要。SegRap2023挑战赛专注于使用单中心配对的平扫CT（ncCT）与增强CT（ceCT）进行OAR与GTV分割，而SegRap2025挑战赛在此基础上，旨在提升分割模型在不同影像中心与成像模态间的泛化性与鲁棒性。SegRap2025包含两项任务：Task01利用SegRap2023数据集中的配对CT进行GTV分割，并引入额外的外部测试集以评估跨中心泛化能力；Task02则专注于LN CTV分割，使用多中心训练数据和一个未见过的外部测试集，其中每个病例包含配对CT扫描或单一模态数据，重点考察跨中心与跨模态的鲁棒性。本文介绍了该挑战赛的设置，并对十个参赛团队提交的解决方案进行了全面分析。在GTV分割任务中，表现最佳的模型在内部与外部测试队列上分别达到了74.61%和56.79%的平均Dice相似系数（DSC）。在LN CTV分割任务中，最佳模型在配对CT、仅ceCT和仅ncCT子集上的最高平均DSC值分别达到了60.24%、60.50%和57.23%。SegRap2025建立了一个大规模、多中心、多模态的基准，用于评估放疗靶区分割的泛化性与鲁棒性，为开发临床可用的自动化放疗计划系统提供了宝贵洞见。该基准可通过以下网址获取：https://hilab-git.github.io/SegRap2025_Challenge。