{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T01:39:59Z","timestamp":1777945199111,"version":"3.51.4"},"reference-count":37,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Kidney tumors, common in the urinary system, have widely varying survival rates post\u2010surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non\u2010invasive, accurate prediction models to assist in clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to construct a K\u2010means clustering algorithm enhanced by Transformer\u2010based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study was based on a publicly available C4KC\u2010KiTS\u20102019 dataset from the TCIA database, including preoperative computed tomography (CT) images and survival time data of 210 patients. Initially, the radiomics features of the kidney tumor area were extracted using the 3D slicer software. Feature selection was then conducted using ICC, mRMR algorithms, and LASSO regression to calculate radiomics scores. Subsequently, the selected features were input into a pre\u2010trained Transformer model for feature transformation to obtain a higher\u2010dimensional feature set. Then, K\u2010means clustering was performed using this feature set, and the model was evaluated using receiver operating characteristic (ROC) and Kaplan\u2013Meier curves. Finally, the SHAP interpretability algorithm was used for the feature importance analysis of the K\u2010means clustering results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eleven important features were selected from 851 radiomics features. The K\u2010means clustering model after Transformer feature transformation showed AUCs of 0.889, 0.841, and 0.926 for predicting 1\u2010, 3\u2010, and 5\u2010year overall survival rates, respectively, thereby outperforming both the K\u2010means model with original feature inputs and the radiomics score method. A clustering analysis revealed survival prognosis differences among different patient groups, and a SHAP analysis provided insights into the features that had the most significant impacts on the model predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The K\u2010means clustering algorithm enhanced by the Transformer feature transformation proposed in this study demonstrates promising accuracy and interpretability in predicting the overall survival rate after kidney tumor resection. This method provides a valuable tool for clinical decision\u2010making and contributes to improved management and treatment strategies for patients with kidney tumors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17639","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T23:28:45Z","timestamp":1738020525000},"page":"3243-3257","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K\u2010means clustering"],"prefix":"10.1002","volume":"52","author":[{"given":"Yiren","family":"Wang","sequence":"first","affiliation":[{"name":"School of Nursing Southwest Medical University  Luzhou China"},{"name":"Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou  Luzhou 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Imaging Southwest Medical University  Luzhou China"},{"name":"Department of Radiology The Affiliated Hospital of Southwest Medical University  Luzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaiwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College Jiangxi Clinical Research Center for Cancer  Nanchang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Zhou","sequence":"additional","affiliation":[{"name":"Wound Healing Basic Research and Clinical Application Key Laboratory of Luzhou  Luzhou China"},{"name":"Department of Radiology The Affiliated Hospital of Southwest Medical University  Luzhou China"},{"name":"Department of Nursing The Affiliated Hospital of Southwest Medical University  Luzhou 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