{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:26:24Z","timestamp":1777418784201,"version":"3.51.4"},"reference-count":38,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T00:00:00Z","timestamp":1747612800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82160336"],"award-info":[{"award-number":["82160336"]}],"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>Microvascular invasion (MVI) is strongly associated with the prognosis of patients with hepatocellular carcinoma (HCC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate the value of Transformer models with Sonazoid contrast\u2010enhanced ultrasound (CEUS) in the preoperative prediction of MVI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective study included 164 HCC patients. Deep learning features and radiomic features were extracted from arterial and Kupffer phase images, alongside the collection of clinicopathological parameters. Normality was assessed using the Shapiro\u2013Wilk test. The Mann\u2012Whitney\n                      <jats:italic>U<\/jats:italic>\n                      \u2010test and least absolute shrinkage and selection operator algorithm were applied to screen features. Transformer, radiomic, and clinical prediction models for MVI were constructed with logistic regression. Repeated random splits followed a 7:3 ratio, with model performance evaluated over 50 iterations. The area under the receiver operating characteristic curve (AUC, 95% confidence interval [CI]), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), decision curve, and calibration curve were used to evaluate the performance of the models. The DeLong test was applied to compare performance between models. The Bonferroni method was used to control type I error rates arising from multiple comparisons. A two\u2010sided\n                      <jats:italic>p<\/jats:italic>\n                      \u2010value of\u00a0&lt;\u00a00.05 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the training set, the diagnostic performance of the arterial\u2010phase Transformer (AT) and Kupffer\u2010phase Transformer (KT) models were better than that of the radiomic and clinical (Clin) models (\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      &lt;\u00a00.0001). In the validation set, both the AT and KT models outperformed the radiomic and Clin models in terms of diagnostic performance (\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      &lt;\u00a00.05). The AUC (95% CI) for the AT model was 0.821 (0.72\u20130.925) with an accuracy of 80.0%, and the KT model was 0.859 (0.766\u20130.977) with an accuracy of 70.0%. Logistic regression analysis indicated that tumor size (\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      =\u00a00.016) and alpha\u2010fetoprotein (AFP) (\n                      <jats:italic>p\u00a0<\/jats:italic>\n                      =\u00a00.046) were independent predictors of MVI.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Transformer models using Sonazoid CEUS have potential for effectively identifying MVI\u2010positive patients preoperatively.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17895","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T04:10:00Z","timestamp":1747627800000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transformer model based on Sonazoid contrast\u2010enhanced ultrasound for microvascular invasion prediction in hepatocellular carcinoma"],"prefix":"10.1002","volume":"52","author":[{"given":"Qiong","family":"Qin","sequence":"first","affiliation":[{"name":"Department of Ultrasound The First Affiliated Hospital of Guangxi Medical University  Nanning Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinshu","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound The First Affiliated Hospital of Guangxi Medical University  Nanning Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingdan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasound The First Affiliated Hospital of Guangxi Medical University  Nanning Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruizhi","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Ultrasound The First Affiliated Hospital of Guangxi 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