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Three models were developed to distinguish HER2\u2010zero, HER2\u2010low, and HER2\u2010positive from their respective non\u2010HER2 categories.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of the model. Evaluation of the model performances for HER2 statuses involved receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC), accuracy, sensitivity, and specificity. The <jats:italic>P<\/jats:italic>\u2010values &lt;0.05 were considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The automatic segmentation network achieved DSC values of 0.85 to 0.90 compared to the manual segmentation across different sets. The deep learning models using ResNetGN achieved AUCs of 0.782, 0.776, and 0.768 in differentiating HER2\u2010zero from others in the training, internal test, and external test sets, respectively. Similarly, AUCs of 0.820, 0.813, and 0.787 were achieved for HER2\u2010low vs. others, and 0.792, 0.745, and 0.781 for HER2\u2010positive vs. others, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The proposed DCE\u2010MRI\u2010based deep learning system may have the potential to preoperatively distinct HER2 expressions of breast cancers with therapeutic implications.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>4<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 3<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29670","type":"journal-article","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T02:45:16Z","timestamp":1733539516000},"page":"2212-2220","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Development and Validation of a Deep Learning System to Differentiate <scp>HER2<\/scp>\u2010Zero, <scp>HER2<\/scp>\u2010Low, and <scp>HER2<\/scp>\u2010Positive Breast Cancer Based on Dynamic Contrast\u2010Enhanced <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1919-1885","authenticated-orcid":false,"given":"Yi","family":"Dai","sequence":"first","affiliation":[{"name":"Department of Medical Imaging Peking University Shenzhen Hospital  Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Lian","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Peking University Shenzhen Hospital  Shenzhen Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Shandong Technology and Business University  Yantai Shandong 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