{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:49:48Z","timestamp":1790268588705,"version":"4.1.0"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82300122"],"award-info":[{"award-number":["82300122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","ejradiology.com","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.ejrad.2025.112004","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T08:11:26Z","timestamp":1740039086000},"page":"112004","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors"],"prefix":"10.1016","volume":"185","author":[{"given":"Qiongliang","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taiyu","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangnan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2025.112004_b0010","unstructured":"[Chinese guideline for clinical diagnosis and treatment of thymic epithelial tumors (2021 Edition)]. Zhonghua Zhong Liu Za Zhi 2021; 43(4): 395-404."},{"issue":"10 Suppl 4","key":"10.1016\/j.ejrad.2025.112004_b0015","doi-asserted-by":"crossref","first-page":"S260","DOI":"10.1097\/JTO.0b013e3181f1f62d","article-title":"Epidemiology of thymoma and associated malignancies","volume":"5","author":"Engels","year":"2010","journal-title":"J Thorac Oncol"},{"issue":"4","key":"10.1016\/j.ejrad.2025.112004_b0020","doi-asserted-by":"crossref","first-page":"641","DOI":"10.21037\/jtd.2016.03.23","article-title":"Management of thymic tumors-consensus based on the Chinese Alliance for Research in Thymomas Multi-institutional retrospective studies","volume":"8","author":"Fang","year":"2016","journal-title":"J Thorac Dis"},{"key":"10.1016\/j.ejrad.2025.112004_b0005","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.critrevonc.2016.01.012","article-title":"Thymoma and thymic carcinomas","volume":"99","author":"Scorsetti","year":"2016","journal-title":"Crit Rev Oncol Hematol"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112004_b0025","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.jtcvs.2009.03.033","article-title":"Comparison of patterns of relapse in thymic carcinoma and thymoma","volume":"138","author":"Huang","year":"2009","journal-title":"J Thorac Cardiovasc Surg"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112004_b0030","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1016\/j.jtho.2021.09.017","article-title":"Introduction to 2021 WHO Classification of Thoracic Tumors","volume":"17","author":"Tsao","year":"2022","journal-title":"J Thorac Oncol"},{"issue":"2","key":"10.1016\/j.ejrad.2025.112004_b0035","doi-asserted-by":"crossref","first-page":"283","DOI":"10.2214\/ajr.183.2.1830283","article-title":"Does CT of thymic epithelial tumors enable us to differentiate histologic subtypes and predict prognosis?","volume":"183","author":"Jeong","year":"2004","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112004_b0040","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.athoracsur.2020.05.088","article-title":"Management and long-term outcomes of advanced stage thymoma in the United States","volume":"111","author":"Khorfan","year":"2021","journal-title":"Ann Thorac Surg"},{"issue":"23","key":"10.1016\/j.ejrad.2025.112004_b0045","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.21037\/atm-21-5999","article-title":"A radiomics model combined with XGBoost may improve the accuracy of distinguishing between mediastinal cysts and tumors: a multicenter validation analysis","volume":"9","author":"Wang","year":"2021","journal-title":"Ann Transl Med"},{"key":"10.1016\/j.ejrad.2025.112004_b0050","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.3389\/fonc.2019.01041","article-title":"Relationship Between Computed Tomography Imaging Features and Clinical Characteristics, Masaoka-Koga Stages, and World Health Organization Histological Classifications of Thymoma","volume":"9","author":"Han","year":"2019","journal-title":"Front Oncol"},{"key":"10.1016\/j.ejrad.2025.112004_b0055","doi-asserted-by":"crossref","first-page":"23428","DOI":"10.1038\/srep23428","article-title":"Reproducibility of radiomics for deciphering tumor phenotype with imaging","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci Rep"},{"key":"10.1016\/j.ejrad.2025.112004_b0060","unstructured":"Y. Chen, L. Quan, C. Long, Y. Chen, L. Zu, C. Huang, Brain tumor segmentation based on the U-NET++ network with efficientnet encoder. (1878-7401 (Electronic))."},{"key":"10.1016\/j.ejrad.2025.112004_b0065","unstructured":"J. Li, W. Sun, K.M. von Deneen, et al. MG-Net: Multi-level global-aware network for thymoma segmentation. (1879-0534 (Electronic))."},{"issue":"2","key":"10.1016\/j.ejrad.2025.112004_b0070","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1148\/radiol.2018181352","article-title":"Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI","volume":"290","author":"Truhn","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2025.112004_b0075","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015; 2015 2015\/\/","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.ejrad.2025.112004_b0080","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016; 2016 2016\/\/","first-page":"424","article-title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.ejrad.2025.112004_b0085","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","article-title":"nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.ejrad.2025.112004_b0090","series-title":"Computer Vision \u2013 ECCV 2022 Workshops; 2023 2023","first-page":"205","article-title":"Swin-Unet: Unet-Like Pure Transformer forMedical Image Segmentation","author":"Cao","year":"2023"},{"key":"10.1016\/j.ejrad.2025.112004_b0095","doi-asserted-by":"crossref","unstructured":"A. Hatamizadeh, Y. Tang, V. Nath, et al. UNETR: Transformers for 3D Medical Image Segmentation. 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV); 2022 3-8 Jan. 2022; 2022. p. 1748-58.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"10.1016\/j.ejrad.2025.112004_b0100","doi-asserted-by":"crossref","unstructured":"K. Patel, A. Kovalyov, I. Panahi, UX-Net: Filter-and-Process-Based Improved U-Net for real-time time-domain audio Separation. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2023 4-10 June 2023; 2023. p. 1-5.","DOI":"10.1109\/ICASSP49357.2023.10095038"},{"issue":"9","key":"10.1016\/j.ejrad.2025.112004_b0105","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TMI.2024.3398728","article-title":"UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation","volume":"43","author":"Shaker","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.ejrad.2025.112004_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106635","article-title":"MG-Net: Multi-level global-aware network for thymoma segmentation","volume":"155","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejrad.2025.112004_b0115","doi-asserted-by":"crossref","DOI":"10.3390\/app10175729","article-title":"Enhancing U-Net with spatial-channel attention gate for abnormal tissue segmentation in medical imaging","author":"Khanh","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.ejrad.2025.112004_b0120","unstructured":"Str\u00f6bel P, Bauer A Fau - Puppe B, Puppe B Fau - Kraushaar T, et al. Tumor recurrence and survival in patients treated for thymomas and thymic squamous cell carcinomas: a retrospective analysis. (0732-183X (Print))."},{"key":"10.1016\/j.ejrad.2025.112004_b0125","unstructured":"Kondo K, Yoshizawa K Fau - Tsuyuguchi M, Tsuyuguchi M Fau - Kimura S, et al. WHO histologic classification is a prognostic indicator in thymoma. (0003-4975 (Print))."},{"key":"10.1016\/j.ejrad.2025.112004_b0130","unstructured":"K. Yasaka, H. Akai, O. Abe, K. Ohtomo, S. Kiryu, Quantitative computed tomography texture analyses for anterior mediastinal masses: Differentiation between solid masses and cysts. (1872-7727 (Electronic))."},{"key":"10.1016\/j.ejrad.2025.112004_b0135","unstructured":"W. Zhao, Y.A.-O. Ozawa, M. Hara, K. Okuda, A. Hiwatashi, Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes. (1867-108X (Electronic))."},{"key":"10.1016\/j.ejrad.2025.112004_b0140","unstructured":"X. Wang, X. You, L. Zhang, et al. A radiomics model combined with XGBoost may improve the accuracy of distinguishing between mediastinal cysts and tumors: a multicenter validation analysis. (2305-5839 (Print))."},{"key":"10.1016\/j.ejrad.2025.112004_b0145","unstructured":"Sadohara J, Fujimoto K Fau - M\u00fcller NL, M\u00fcller Nl Fau - Kato S, et al. Thymic epithelial tumors: comparison of CT and MR imaging findings of low-risk thymomas, high-risk thymomas, and thymic carcinomas. (0720-048X (Print))."},{"key":"10.1016\/j.ejrad.2025.112004_b0150","unstructured":"Tomiyama N, Johkoh T Fau - Mihara N, Mihara N Fau - Honda O, et al. Using the World Health Organization Classification of thymic epithelial neoplasms to describe CT findings. (0361-803X (Print))."},{"key":"10.1016\/j.ejrad.2025.112004_b0155","unstructured":"Abdel Razek AA, Khairy M Fau - Nada N, Nada N. Diffusion-weighted MR imaging in thymic epithelial tumors: correlation with World Health Organization classification and clinical staging. (1527-1315 (Electronic))."},{"key":"10.1016\/j.ejrad.2025.112004_b0160","unstructured":"C. Yu, T. Li, X. Yang, et al. Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors. (1471-2342 (Electronic))."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25000907?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25000907?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T12:05:35Z","timestamp":1741781135000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X25000907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":32,"alternative-id":["S0720048X25000907"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112004","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated segmentation by SCA-UNet can be directly used for radiomics diagnosis of thymic epithelial tumors","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 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":"112004"}}