这项研究旨在比较不同机器学习（ML）模型和名词图表对预测男性乳腺癌（MBC）患者的远处转移能力，并使用SHapley Additive exPlanations（SHAP）框架解释最优ML模型。使用来自SEER数据库2010年至2015年的男性乳腺癌（MBC）患者数据和我们医院2010年至2020年的MBC患者数据开发了四种强大的ML模型。使用曲线下面积（AUC）和Brier评分评估不同模型的能力。应用Delong测试比较模型的性能。使用逻辑回归进行单变量和多变量分析。分析了2351名患者，其中168名（7.1％）有远处转移（M1），117名（5.0％）有骨转移，71名（3.0％）有肺转移。诊断时的中位年龄为68.0岁。大多数患者未接受放疗（1723，73.3％）或化疗（1447，61.5％）。 XGB模型是预测MBC患者M1的最佳ML模型。在十倍交叉验证（AUC：0.884；SD：0.02）、训练（AUC：0.907；95％CI：0.899-0.917）、测试（AUC：0.827；95％CI：0.802-0.857）和外部验证（AUC：0.754；95％CI：0.739-0.771）集中显示了最大的AUC值。它还在预测骨转移（训练集AUC：0.880，95％CI：0.856-0.903；测试集AUC：0.823，95％CI：0.790-0.848；外部验证集AUC：0.747，95％CI：0.727-0.764）和肺转移（训练集AUC：0.906，95％CI：0.877-0.928；测试集AUC：0.859，95％CI：0.816-0.891；外部验证集AUC：0.756，95％CI：0.732-0.777）方面表现出强大的能力。 XGB模型的AUC值大于名词图表在训练（0.907比0.802）和外部验证（0.754比0.706）集中的AUC值。 XGB模型比其他ML模型和名词图表更好地预测MBC患者的远处转移，此外，XGB模型对于预测骨转移和肺转移也具有强大的模型能力。与SHAP值相结合，可以帮助医生直观地了解每个变量对结果的影响。 © 2023年，作者。

This research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model by SHapley Additive exPlanations (SHAP) framework.Four powerful ML models were developed using data from male breast cancer (MBC) patients in the SEER database between 2010 and 2015 and MBC patients from our hospital between 2010 and 2020. 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.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 years 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: 0.899-0.917), testing (AUC:0.827; 95% CI: 0.802-0.857) and external validation (AUC:0.754; 95% CI: 0.739-0.771) sets. It also showed powerful ability in the prediction of bone metastasis (AUC: 0.880, 95% CI: 0.856-0.903 in the training set; AUC: 0.823, 95% CI:0.790-0.848 in the test set; AUC: 0.747, 95% CI: 0.727-0.764 in the external validation set) and lung metastasis (AUC: 0.906, 95% CI: 0.877-0.928 in training set; AUC: 0.859, 95% CI: 0.816-0.891 in the test set; AUC: 0.756, 95% CI: 0.732-0.777 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.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.© 2023. The Author(s).