[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124603-en":3,"doc-seo-124603-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124603,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan","The study compared radiomic analysis with machine learning models against a three-dimensional convolutional neural network (3D CNN) for differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs) using chest computed tomography. A retrospective cohort drew clinical variables and pathology-based labels, then separated unenhanced and contrast-enhanced CT datasets. Performance was assessed with macro F1-score and ROC analysis, showing consistent advantages for a LightGBM model with Extra Trees across both imaging settings.","TYPE Original Research PUBLISHED 18 April 2023  \nDOI 10.3389/fonc.2023.1105100  \nOPEN ACCESS  \nEDITED BY Alla Reznik,  \nLakehead University, Canada  \nREVIEWED BY YuChuan Hu,  \nFourth Military Medical University, China Wei Yang,  \nBeijing Cancer Hospital, Peking University, China  \n*CORRESPONDENCE Yi-Ting Yen  \n [b85401067@gmail.com](b85401067@gmail.com)[ ](b85401067@gmail.com)Mi-Chia Ma  \n [mcma@mail.ncku.edu.tw](mcma@mail.ncku.edu.tw)  \n†These authors have contributed equally to this work and share first authorship  \nSPECIALTY SECTION  \nThis article was submitted to Cancer Imaging and  \nImage-directed Interventions, a section of the journal Frontiers in Oncology  \nRECEIVED 22 November 2022  \nACCEPTED 27 March 2023  \nPUBLISHED 18 April 2023  \nCITATION  \nChang C-C, Tang E-K, Wei Y-F, Lin C-Y, Wu F-Z, Wu M-T, Liu Y-S, Yen Y-T, Ma M-C and Tseng Y-L (2023) Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.  \nFront. Oncol. 13:1105100 .  \ndoi: 10.3389/fonc.2023.1105100  \nCOPYRIGHT  \n© 2023 Chang, Tang, Wei, Lin, Wu, Wu, Liu, Yen, Ma and Tseng. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nClinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan  \nChao-Chun Chang 1†, En-Kuei Tang2†, Yu-Feng Wei 3,4†, Chia-Ying Lin 5†, Fu-Zong Wu6,7,8, Ming-Ting Wu 6,9,10, Yi-Sheng Liu 5, Yi-Ting Yen 1,11*, Mi-Chia Ma 12* and Yau-Lin Tseng 1  \n1 Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, 2 Division of Thoracic Surgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 3School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan,  \n4 Division of Chest Medicine, Department of Internal Medicine, E-Da Cancer Hospital, Kaohsiung, Taiwan, 5 Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, 6 Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, 7 Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 8 Institute of Education, National Sun Yat-sen University, Kaohsiung, Taiwan, 9School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan,  \n10 Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 11 Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan, 12 Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan  \nPurpose: To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs) .  \nMethods: A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019 . 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