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Inclusion criteria were: patients with breast cancer; deep learning using MRI images was applied to predict axillary lymph nodes metastases; sufficient data were present; original research articles. Quality Assessment of Diagnostic Accuracy Studies-AI and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. A summary receiver operating characteristic curve (SROC) was performed. R statistical software (version 4.4.0) was used for statistical analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 10 studies were included. The pooled sensitivity and specificity were 0.76 (95% CI, 0.67\u20130.83) and 0.81 (95% CI, 0.74\u20130.87), respectively, with both measures having moderate between-study heterogeneity (I\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u200961% and 60%, respectively;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.01). The SROC analysis yielded a weighted AUC of 0.788.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This meta-analysis demonstrates that deep learning algorithms applied to breast MRI offer promising diagnostic performance for predicting axillary lymph node metastases in breast cancer patients. Incorporating deep learning into clinical practice may enhance decision-making by providing a non-invasive method to more accurately predict lymph node involvement, potentially reducing unnecessary surgeries.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s40644-025-00863-3","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T02:47:14Z","timestamp":1743389234000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"Chia-Fen","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Len","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shou-Tung","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Te","family":"Chou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dar-Ren","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Pei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"issue":"16_suppl","key":"863_CR1","doi-asserted-by":"publisher","first-page":"10528-","DOI":"10.1200\/JCO.2023.41.16_suppl.10528","volume":"41","author":"ET Sedeta","year":"2023","unstructured":"Sedeta ET, Jobre B, Avezbakiyev B. 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