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The area under curve (AUC) and Brier score were used to assess the capacity of different models. The Delong test was applied to compare the performance of the models. Univariable and multivariable analysis were conducted using logistic regression.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Of 2351 patients were analyzed; 168 (7.1%) had distant metastasis (M1); 117 (5.0%) had bone metastasis, and 71 (3.0%) had lung metastasis. The median age at diagnosis is 68.0\u00a0years old. Most patients did not receive radiotherapy (1723, 73.3%) or chemotherapy (1447, 61.5%). The XGB model was the best ML model for predicting M1 in MBC patients. It showed the largest AUC value in the tenfold cross validation (AUC:0.884; SD:0.02), training (AUC:0.907; 95% CI: 0.899\u20140.917), testing (AUC:0.827; 95% CI: 0.802\u20140.857) and external validation (AUC:0.754; 95% CI: 0.739\u20140.771) sets. It also showed powerful ability in the prediction of bone\u00a0metastasis (AUC: 0.880, 95% CI: 0.856\u20140.903 in the training set; AUC: 0.823, 95% CI:0.790\u20140.848 in the test set; AUC: 0.747, 95% CI: 0.727\u20140.764 in the external validation set) and lung metastasis (AUC: 0.906, 95% CI: 0.877\u20140.928 in training set; AUC: 0.859, 95% CI: 0.816\u20140.891 in the test set; AUC: 0.756, 95% CI: 0.732\u20140.777 in the external validation set). The AUC value of the XGB model was larger than that of nomogram in the training (0.907 vs 0.802) and external validation (0.754 vs 0.706) sets.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The XGB model is a better predictor of distant metastasis among MBC patients than other ML models and nomogram; furthermore, the XGB model is a powerful model for predicting bone and lung metastasis. Combining with SHAP values, it could help doctors intuitively understand the impact of each variable on outcome.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02166-8","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T11:02:43Z","timestamp":1682074963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model"],"prefix":"10.1186","volume":"23","author":[{"given":"Xuhai","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"2166_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","volume":"72","author":"RL Siegel","year":"2022","unstructured":"Siegel RL, Miller KD, Fuchs HE, Jemal A. 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The ethics committee of Harbin Medical University Cancer Hospital approved this study. It complies with the World Medical Association Declaration of Helsinki in 1964 and subsequently amended versions. An informed consent form (Titled: Informed consent for secondary utilization of medical history data\/biological specimens) was signed by all of the patients from our hospital before the treatment, and a PDF version of this informed consent form is demonstrated in the related files (Chinese and English versions). According to the informed consent form, all the patients consent that the medical history data could be used for scientific research. No biological specimens were used in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not Applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"74"}}