{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T07:48:05Z","timestamp":1782114485963,"version":"3.54.5"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.media.2026.104121","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T07:03:51Z","timestamp":1778137431000},"page":"104121","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["SegRap2025: A benchmark of gross tumor volume and lymph node clinical target volume Segmentation for Radiotherapy Planning of nasopharyngeal carcinoma"],"prefix":"10.1016","volume":"112","author":[{"given":"Jia","family":"Fu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Litingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huamin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyuan","family":"Bian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijun","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunbin","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yicheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elias","family":"Tappeiner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Qayyum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moona","family":"Mazher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven A.","family":"Niederer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junqiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohu","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqiu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-158X","authenticated-orcid":false,"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"27","key":"10.1016\/j.media.2026.104121_b1","doi-asserted-by":"crossref","first-page":"2940","DOI":"10.1200\/JCO.2013.53.5633","article-title":"Randomized phase III trial of concurrent accelerated radiation plus cisplatin with or without cetuximab for stage III to IV head and neck carcinoma: RTOG 0522","volume":"32","author":"Ang","year":"2014","journal-title":"J. Clin. Oncol."},{"key":"10.1016\/j.media.2026.104121_b2","series-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge","author":"Bakas","year":"2018"},{"issue":"3","key":"10.1016\/j.media.2026.104121_b3","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.ijrobp.2020.10.005","article-title":"Generating high-quality lymph node clinical target volumes for head and neck cancer radiation therapy using a fully automated deep learning-based approach","volume":"109","author":"Cardenas","year":"2021","journal-title":"Int. J. Radiat. Oncology* Biology* Phys."},{"issue":"11","key":"10.1016\/j.media.2026.104121_b4","doi-asserted-by":"crossref","first-page":"13408","DOI":"10.1109\/TPAMI.2023.3289667","article-title":"Adaptive region-specific loss for improved medical image segmentation","volume":"45","author":"Chen","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.104121_b5","series-title":"SAM-Med2D","author":"Cheng","year":"2023"},{"issue":"10022","key":"10.1016\/j.media.2026.104121_b6","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1016\/S0140-6736(15)00055-0","article-title":"Nasopharyngeal carcinoma","volume":"387","author":"Chua","year":"2016","journal-title":"Lancet"},{"issue":"9","key":"10.1016\/j.media.2026.104121_b7","doi-asserted-by":"crossref","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.104121_b8","series-title":"LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification","author":"Dorent","year":"2024"},{"issue":"1","key":"10.1016\/j.media.2026.104121_b9","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.radonc.2013.10.010","article-title":"Delineation of the neck node levels for head and neck tumors: a 2013 update. DAHANCA, EORTC, HKNPCSG, NCIC CTG, NCRI, RTOG, TROG consensus guidelines","volume":"110","author":"Gr\u00e9goire","year":"2014","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.media.2026.104121_b10","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R., 2022. Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 16000\u201316009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"issue":"2","key":"10.1016\/j.media.2026.104121_b11","first-page":"3","article-title":"LoRA: Low-rank adaptation of large language models","volume":"1","author":"Hu","year":"2022","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.media.2026.104121_b12","series-title":"STU-Net: Scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training","author":"Huang","year":"2023"},{"issue":"2","key":"10.1016\/j.media.2026.104121_b13","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nature Methods"},{"key":"10.1016\/j.media.2026.104121_b14","doi-asserted-by":"crossref","unstructured":"Isensee, F., Wald, T., Ulrich, C., Baumgartner, M., Roy, S., Maier-Hein, K., Jaeger, P.F., 2024. nnU-Net revisited: A call for rigorous validation in 3D medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 488\u2013498.","DOI":"10.1007\/978-3-031-72114-4_47"},{"issue":"29","key":"10.1016\/j.media.2026.104121_b15","doi-asserted-by":"crossref","first-page":"3356","DOI":"10.1200\/JCO.2015.60.9347","article-title":"Management of nasopharyngeal carcinoma: current practice and future perspective","volume":"33","author":"Lee","year":"2015","journal-title":"J. Clin. Oncol."},{"issue":"1","key":"10.1016\/j.media.2026.104121_b16","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1038\/s41597-025-05815-x","article-title":"A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation","volume":"12","author":"Li","year":"2025","journal-title":"Sci. Data"},{"issue":"4","key":"10.1016\/j.media.2026.104121_b17","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.ijrobp.2022.03.031","article-title":"Automatic delineation of gross tumor volume based on magnetic resonance imaging by performing a novel semisupervised learning framework in nasopharyngeal carcinoma","volume":"113","author":"Liao","year":"2022","journal-title":"Int. J. Radiat. Oncology* Biology* Phys."},{"issue":"3","key":"10.1016\/j.media.2026.104121_b18","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1148\/radiol.2019182012","article-title":"Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma","volume":"291","author":"Lin","year":"2019","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.media.2026.104121_b19","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.ijrobp.2017.11.004","article-title":"Delineation of neck clinical target volume specific to nasopharyngeal carcinoma based on lymph node distribution and the international consensus guidelines","volume":"100","author":"Lin","year":"2018","journal-title":"Int. J. Radiat. Oncology* Biology* Phys."},{"key":"10.1016\/j.media.2026.104121_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103447","article-title":"SegRap2023: A benchmark of organs-at-risk and gross tumor volume segmentation for radiotherapy planning of nasopharyngeal carcinoma","volume":"101","author":"Luo","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104121_b21","doi-asserted-by":"crossref","unstructured":"Luo, Z., Gao, Z., Liao, W., Zhang, S., Wang, G., Luo, X., 2025. Dynamic Gradient Sparsification Training for Few-Shot Fine-Tuning of CT Lymph Node Segmentation Foundation Model. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 165\u2013174.","DOI":"10.1007\/978-3-032-04971-1_16"},{"key":"10.1016\/j.media.2026.104121_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2023.109480","article-title":"Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: A large-scale and multi-center study","volume":"180","author":"Luo","year":"2023","journal-title":"Radiother. Oncol."},{"issue":"1","key":"10.1016\/j.media.2026.104121_b23","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1038\/s41597-024-03890-0","article-title":"A multicenter dataset for lymph node clinical target volume delineation of nasopharyngeal carcinoma","volume":"11","author":"Luo","year":"2024","journal-title":"Sci. Data"},{"issue":"5","key":"10.1016\/j.media.2026.104121_b24","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1016\/j.ijrobp.2024.11.064","article-title":"Generalizable magnetic resonance imaging-based nasopharyngeal carcinoma delineation: Bridging gaps across multiple centers and raters with active learning","volume":"121","author":"Luo","year":"2025","journal-title":"Int. J. Radiat. Oncology* Biology* Phys."},{"key":"10.1016\/j.media.2026.104121_b25","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A., 2016. V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision. 3DV, pp. 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"key":"10.1016\/j.media.2026.104121_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102336","article-title":"Head and neck tumor segmentation in PET\/CT: the HECKTOR challenge","volume":"77","author":"Oreiller","year":"2022","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.media.2026.104121_b27","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1002\/mp.12197","article-title":"Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015","volume":"44","author":"Raudaschl","year":"2017","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2026.104121_b28","doi-asserted-by":"crossref","unstructured":"Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., Petersen, J., Isensee, F., Jaeger, P.F., Maier-Hein, K.H., 2023. MedNeXt: transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 405\u2013415.","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"10.1016\/j.media.2026.104121_b29","first-page":"21081","article-title":"CircleGAN: Generative adversarial learning across spherical circles","volume":"33","author":"Shim","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"9","key":"10.1016\/j.media.2026.104121_b30","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.14474","article-title":"Landmark-based auto-contouring of clinical target volumes for radiotherapy of nasopharyngeal cancer","volume":"25","author":"Sjogreen","year":"2024","journal-title":"J. Appl. Clin. Med. Phys."},{"issue":"22","key":"10.1016\/j.media.2026.104121_b31","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.3390\/cancers14225501","article-title":"Deep learning for automated elective lymph node level segmentation for head and neck cancer radiotherapy","volume":"14","author":"Strijbis","year":"2022","journal-title":"Cancers"},{"issue":"3","key":"10.1016\/j.media.2026.104121_b32","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.ijrobp.2019.06.2549","article-title":"The association between the development of radiation therapy, image technology, and chemotherapy, and the survival of patients with nasopharyngeal carcinoma: a cohort study from 1990 to 2012","volume":"105","author":"Sun","year":"2019","journal-title":"Int. J. Radiat. Oncology* Biology* Phys."},{"issue":"10","key":"10.1016\/j.media.2026.104121_b33","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","article-title":"Clinically applicable deep learning framework for organs at risk delineation in CT images","volume":"1","author":"Tang","year":"2019","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"10.1016\/j.media.2026.104121_b34","doi-asserted-by":"crossref","first-page":"10117","DOI":"10.1038\/s41598-017-10371-5","article-title":"Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer","volume":"7","author":"Vallieres","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2026.104121_b35","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.radonc.2020.10.007","article-title":"Deep learning for elective neck delineation: More consistent and time efficient","volume":"153","author":"Van der Veen","year":"2020","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.media.2026.104121_b36","doi-asserted-by":"crossref","unstructured":"Wahid, K.A., Dede, C., El-Habashy, D.M., Kamel, S., Rooney, M.K., Khamis, Y., Abdelaal, M.R., Ahmed, S., Corrigan, K.L., Chang, E., et al., 2024. Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications. pp. 1\u201335.","DOI":"10.1007\/978-3-031-83274-1_1"},{"issue":"12","key":"10.1016\/j.media.2026.104121_b37","doi-asserted-by":"crossref","first-page":"4078","DOI":"10.1109\/TMI.2024.3412923","article-title":"Dual-reference source-free active domain adaptation for nasopharyngeal carcinoma tumor segmentation across multiple hospitals","volume":"43","author":"Wang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.media.2026.104121_b38","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/TMI.2025.3558775","article-title":"HiCur-NPC: Hierarchical feature fusion curriculum learning for multi-modal foundation model in nasopharyngeal carcinoma","volume":"44","author":"Wang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104121_b39","doi-asserted-by":"crossref","first-page":"6041","DOI":"10.1109\/TIP.2025.3610249","article-title":"Volume fusion-based self-supervised pretraining for 3D medical image segmentation","volume":"34","author":"Wang","year":"2025","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"10.1016\/j.media.2026.104121_b40","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1002\/pro6.1123","article-title":"Guidelines for radiotherapy of nasopharyngeal carcinoma","volume":"5","author":"Wang","year":"2021","journal-title":"Precis. Radiat. Oncol."},{"key":"10.1016\/j.media.2026.104121_b41","doi-asserted-by":"crossref","unstructured":"Wang, L., Liao, W., Zhang, S., Wang, G., 2024. Head and neck tumor segmentation of MRI from pre-and mid-radiotherapy with pre-training, data augmentation and dual flow UNet. In: Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications. pp. 75\u201386.","DOI":"10.1007\/978-3-031-83274-1_5"},{"key":"10.1016\/j.media.2026.104121_b42","series-title":"SAM-aware test-time adaptation for universal medical image segmentation","author":"Wu","year":"2025"},{"issue":"1","key":"10.1016\/j.media.2026.104121_b43","doi-asserted-by":"crossref","first-page":"6137","DOI":"10.1038\/s41467-022-33178-z","article-title":"Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study","volume":"13","author":"Ye","year":"2022","journal-title":"Nat. Commun."},{"issue":"10","key":"10.1016\/j.media.2026.104121_b44","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/JPROC.2024.3507831","article-title":"Domain generalization for medical image analysis: A review","volume":"112","author":"Yoon","year":"2024","journal-title":"Proc. IEEE"},{"issue":"3","key":"10.1016\/j.media.2026.104121_b45","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2026.104121_b46","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.radonc.2022.04.026","article-title":"Level Ib CTV delineation in nasopharyngeal carcinoma based on lymph node distribution and topographic anatomy","volume":"172","author":"Zhao","year":"2022","journal-title":"Radiother. Oncol."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526001908?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526001908?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T07:06:30Z","timestamp":1782111990000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526001908"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":46,"alternative-id":["S1361841526001908"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104121","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SegRap2025: A benchmark of gross tumor volume and lymph node clinical target volume Segmentation for Radiotherapy Planning of nasopharyngeal carcinoma","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104121","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104121"}}