{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T12:30:19Z","timestamp":1782909019599,"version":"3.54.5"},"reference-count":39,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>Our study aims to investigate the impact of B\u2010mode ultrasound (B\u2010US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Totally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet\u201018 network was employed to predict four molecular subtypes from B\u2010US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1\u2010scores, and confusion matrix.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on B\u2010US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1\u2010scores is 0.73. By combining B\u2010US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B\u2010US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40\u2009years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40\u2009years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Multimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B\u2010US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40\u2009years old compared with those who are 40\u2009years old and above.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jum.16460","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T07:25:25Z","timestamp":1712388325000},"page":"1375-1388","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Assessing the Influence of <scp>B\u2010US<\/scp>, <scp>CDFI<\/scp>, <scp>SE,<\/scp> and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4202-574X","authenticated-orcid":false,"given":"Weiyong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine University of Science and Technology of China  Hefei 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