{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:02:10Z","timestamp":1789534930360,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC2416500"],"award-info":[{"award-number":["2023YFC2416500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901021"],"award-info":[{"award-number":["61901021"]}],"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":["62371027"],"award-info":[{"award-number":["62371027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JKF\u201020240571"],"award-info":[{"award-number":["JKF\u201020240571"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Ultrasound Medicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>BI\u2010RADS 4a+ breast tumors have a high rate of unnecessary biopsies due to ambiguous diagnostic features. This study aims to develop and evaluate a computer\u2010aided diagnosis method integrating wavelet spectrum analysis with deep learning for benign\u2010malignant classification of these tumors, so as to reduce unnecessary invasive biopsies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective study was conducted on 390 patients with breast lesions categorized as BI\u2010RADS 4a or higher. We proposed a computerized diagnostic method that combines wavelet spectrum analysis with deep learning. A 3\u2010channel ultrasound image was constructed by integrating B\u2010mode, high\u2010frequency (13\u2009MHz), and low\u2010frequency (7\u2009MHz) wavelet components. This multi\u2010channel input was processed by a dual\u2010channel deep learning network for tumor classification. A strict patient\u2010specific biopsy\u2010sparing criterion was also established and evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The integration of wavelet spectral features improved tumor classification performance by an average of 2% compared to using B\u2010mode images alone. The proposed model achieved an overall accuracy of 84.52%. Under a patient\u2010specific evaluation framework more aligned with clinical practice, the model demonstrated a biopsy\u2010sparing rate of 26.01% while maintaining a low missed diagnosis rate of 1.786% at the most stringent threshold. The biopsy\u2010sparing to missed diagnosis rate ratio remained high across thresholds, confirming the robustness of the patient\u2010based assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The integration of wavelet spectrum analysis into a multi\u2010channel deep learning network effectively improves the classification of BI\u2010RADS 4a or higher breast tumors. This method demonstrates significant potential as a non\u2010invasive adjunct tool to enhance diagnostic precision and reduce unnecessary biopsies in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jum.70315","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T09:12:02Z","timestamp":1781169122000},"page":"2299-2310","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Wavelet Spectrum in a Multi\u2010Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors With\n                    <scp>BI<\/scp>\n                    \u2010\n                    <scp>RADS<\/scp>\n                    Category 4a or Higher"],"prefix":"10.1002","volume":"45","author":[{"given":"Yina","family":"Li","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering Beihang University  Beijing 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