我们测试了普通机器学习和联合机器学习算法在肺腺癌患者骨转移分类中的表现。我们使用R版本3.5.3进行一般信息的统计分析，并使用Python构建机器学习模型。我们首先使用4种机器学习算法的平均分类器来排列特征，结果显示种族、性别、是否接受手术和婚姻是影响骨转移的前4个因素。机器学习在训练组中的结果为：对于曲线下面积（AUC），除RF和LR外，所有机器学习分类器的AUC值均大于0.8，但联合算法并未改善任何单一机器学习算法的AUC值。在准确性和精度方面的结果中，除RF算法外，其他机器学习分类器的准确率均高于70％，只有LGBM算法的精度高于70％。机器学习在测试组中的结果类似，在曲线下面积（AUC）方面，除RF和LR外，所有机器学习分类器的AUC值均大于0.8，但联合算法并未改善任何单一机器学习算法的AUC值。在准确性方面，除RF算法外，其他机器学习分类器的准确率均高于70％。LGBM算法的最高精度为0.675。这项概念验证研究的结果表明，机器学习算法分类器可以区分肺癌患者的骨转移。这将为未来使用非侵入性技术识别肺癌骨转移提供新的研究思路。然而，还需要更多前瞻性多中心队列研究。

We tested the performance of general machine learning and joint machine learning algorithms in the classification of bone metastasis, in patients with lung adenocarcinoma.We used R version 3.5.3 for statistical analysis of the general information, and Python to construct machine learning models.We first used the average classifiers of the 4 machine learning algorithms to rank the features and the results showed that race, sex, whether they had surgery and marriage were the first 4 factors affecting bone metastasis. Machine learning results in the training group: for area under the curve (AUC), except for RF and LR, the AUC values of all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC for any single machine learning algorithm. Among the results related to accuracy and precision, the accuracy of other machine learning classifiers except the RF algorithm was higher than 70%, and only the precision of the LGBM algorithm was higher than 70%. Machine learning results in the test group: Similarly, for areas under the curve (AUC), except RF and LR, the AUC values for all machine learning classifiers were greater than .8, but the joint algorithm did not improve the AUC value for any single machine learning algorithm. For accuracy, except for the RF algorithm, the accuracy of other machine learning classifiers was higher than 70%. The highest precision for the LGBM algorithm was .675.The results of this concept verification study show that machine learning algorithm classifiers can distinguish the bone metastasis of patients with lung cancer. This will provide a new research idea for the future use of non-invasive technology to identify bone metastasis in lungcancer. However, more prospective multicenter cohort studies are needed.