{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:55:40Z","timestamp":1774032940341,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":["82071892"],"award-info":[{"award-number":["82071892"]}],"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":["82171920"],"award-info":[{"award-number":["82171920"]}],"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":["82271941"],"award-info":[{"award-number":["82271941"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Pathological axillary lymph node (pALN) burden is an important factor for treatment decision\u2010making in clinical T1\u2010T2 (cT1\u2010T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1\u2010T2 stage breast cancer.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>A total of 506 females (range: 24\u201383\u2009years) with cT1\u2010T2 stage breast cancer from two institutions, forming the training (<jats:italic>N<\/jats:italic>\u2009=\u2009340), internal validation (<jats:italic>N<\/jats:italic>\u2009=\u200985), and external validation cohorts (<jats:italic>N<\/jats:italic>\u2009=\u200981), respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>This study used 1.5\u2010T, axial fat\u2010suppressed T2\u2010weighted turbo spin\u2010echo sequence and axial three\u2010dimensional dynamic contrast\u2010enhanced fat\u2010suppressed T1\u2010weighted gradient echo sequence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k\u2010Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best\u2010performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease\u2010free survival (DFS) were examined.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Chi\u2010squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer\u2010Lemeshow test, log\u2010rank, Cox regression analyses, and intraclass correlation coefficient were performed. A <jats:italic>P<\/jats:italic>\u2010value &lt;0.05 was considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059\u201315.207).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1\u2010T2 stage breast cancer patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29554","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T03:30:31Z","timestamp":1724383831000},"page":"1221-1231","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessing Axillary Lymph Node Burden and Prognosis in <scp>cT1<\/scp>\u2010<scp>T2<\/scp> Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual\u2010Institutional <scp>MRI<\/scp> Study"],"prefix":"10.1002","volume":"61","author":[{"given":"Jiayi","family":"Liao","sequence":"first","affiliation":[{"name":"Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences  Guangzhou China"},{"name":"Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University  Guangzhou China"},{"name":"Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences  Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan  Kunming 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