{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T19:33:55Z","timestamp":1779910435433,"version":"3.53.1"},"reference-count":52,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82260358"],"award-info":[{"award-number":["82260358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82171906"],"award-info":[{"award-number":["82171906"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate classification of high\u2010risk and low\u2010risk thymomas is critical for guiding treatment strategies and assessing prognosis. Thymoma is the most common primary tumor of the anterior mediastinum. However, previous studies have limitations in comprehensively utilizing imaging data, particularly in combining radiomics and deep learning (DL) features for preoperative classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a comprehensive model based on computed tomography (CT) imaging data (CSRT, Clinical Semantic, Radiomics, and Vision Transformer) to enhance the accuracy of preoperative high\u2010risk and low\u2010risk classification of thymomas and evaluate its application in non\u2010invasive diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 360 patients with pathologically confirmed thymomas from three centers, with 274 cases (Centers A and B) used for model training and 86 cases (Center C) serving as an external validation set. CT images, including non\u2010contrast enhanced CT (NECT) and contrast\u2010enhanced CT (CECT), were used to extract radiomics features and ViT\u2010based DL features, along with calculated Delta features (NECT minus CECT). Clinical semantic features were integrated, and key features were selected using t\u2010tests and least absolute shrinkage and selection operator (LASSO) regression to construct the fusion model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The CSRT model demonstrated excellent performance in the independent validation cohort, achieving an area under the receiver operating characteristic curve (AUC) of 0.835, an accuracy of 77.9%, a sensitivity of 78.4%, and a specificity of 77.1%. Calibration curves indicated high consistency between predictions and actual classifications. Through decision curve analysis, the model exhibited a high net benefit when the threshold probability exceeded 30%, confirming its clinical utility.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The CSRT model effectively differentiates between high\u2010risk and low\u2010risk thymomas preoperatively using CT imaging data. This non\u2010invasive diagnostic tool supports individualized treatment strategies and enhances clinical decision\u2010making, offering significant value for thymoma management by providing reliable classification above clinically relevant risk thresholds.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17968","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T04:43:10Z","timestamp":1752640990000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Risk classification of thymoma based on multi\u2010feature fusion in dynamic enhanced CT"],"prefix":"10.1002","volume":"52","author":[{"given":"Xiayan","family":"Peng","sequence":"first","affiliation":[{"name":"School of Life &amp; Environmental Science Guilin University of Electronic Technology  Guilin Guangxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"He","sequence":"additional","affiliation":[{"name":"Rehabilitation Medicine Center Department of Radiology Zhejiang Provincial People's Hospital Affiliated People's Hospital Hangzhou Medical College  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"Rehabilitation Medicine Center Department of Radiology Zhejiang Provincial People's Hospital Affiliated People's Hospital Hangzhou Medical College  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongshun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiology Guangzhou First People's Hospital  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pathology Zhejiang Provincial People's Hospital Affiliated People's Hospital Hangzhou Medical College  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shumin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangying","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology State Key Laboratory of Oncology in South China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Sun Yat\u2010sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuchao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life &amp; 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