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Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k\u2010Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best\u2010performing of the four models to analyze the association with DFS.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Chi\u2010squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan\u2013Meier curve, log\u2010rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A\n P<\/jats:italic>\n \u2010value <0.05 was considered statistically significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22\u20135.80).<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence<\/jats:title>\n 3<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.28647","type":"journal-article","created":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T23:34:50Z","timestamp":1676590490000},"page":"1580-1589","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and\n MRI<\/scp>\n Characteristics"],"prefix":"10.1002","volume":"58","author":[{"given":"Zeyan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Medicine South China University of Technology Guangzhou China"},{"name":"Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center Kunming China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Wu","sequence":"additional","affiliation":[{"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 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