Qie Shuai, Zhang Xin, Luan Jiusong, Song Zhelun, Li Jingyun, Wang Jingyu
Department of Radiation Oncology, Affiliated Hospital of Hebei University, Baoding, Hebei Province, PR China.
Medicine (Baltimore). 2025 Mar 21;104(12):e41987. doi: 10.1097/MD.0000000000041987.
The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cancer (SCLC) and create a straightforward web-based predictor based on the developed algorithm. Data comprising demographic and clinicopathological characteristics of patients with SCLC and their potential BM were extracted from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. This data was then utilized to develop 12 machine learning algorithm models: support vector machine, logistic regression, NaiveBayes, extreme gradient boosting, decision tree, random forest, ExtraTrees, LightGBM, GradientBoosting, AdaBoost, MLP, and k-nearest neighbor. The models were compared and evaluated using various metrics, including accuracy, precision, recall rate, F1-score, the area under the receiver operating characteristic curve (AUC) value, and the Brier score. The objective was to predict the likelihood of BM in SCLC patients based on their demographic and clinicopathological features. The best-performing model was then chosen, and the associations between the clinicopathological characteristics and the target variable (presence or absence of BM) were interpreted based on this model. This analysis aimed to provide insights into the factors that may influence the risk of BM in SCLC patients. A total of 89,366 SCLC patients were included in this study, and among them, 8269 (9.25%) patients developed BM. The age, T stage, N stage, liver metastasis, lung metastasis, marital status, income, M stage, American Joint Committee on Cancer stage, and brain metastasis were identified as independent risk factors for SCLC. Among the various predictive models evaluated, the machine learning model utilizing the XGB algorithm showed the highest performance in both internal and external data validation, achieving AUC scores of training set AUC: 0.965, validation set AUC: 0.962, and testing set AUC: 0.961. Subsequently, the XGB algorithm was utilized to develop a web-based predictor for BM in patients with SCLC. This study has developed a web-based predictor utilizing the XGB algorithm to forecast the risk of BM in SCLC patients, aiming to provide doctors with valuable assistance in clinical decision-making.
本研究的目的是设计一种在预测小细胞肺癌（SCLC）骨转移（BM）方面具有卓越性能的机器学习算法，并基于所开发的算法创建一个简单的基于网络的预测工具。从2010年至2018年的监测、流行病学和最终结果数据库中提取了包含SCLC患者的人口统计学和临床病理特征及其潜在BM的数据。然后利用这些数据开发了12种机器学习算法模型：支持向量机、逻辑回归、朴素贝叶斯、极端梯度提升、决策树、随机森林、ExtraTrees、LightGBM、梯度提升、AdaBoost、多层感知器和k近邻。使用各种指标对模型进行比较和评估，包括准确率、精确率、召回率、F1分数、受试者工作特征曲线（AUC）下的面积值和布里尔分数。目的是根据SCLC患者的人口统计学和临床病理特征预测其发生BM的可能性。然后选择性能最佳的模型，并基于该模型解释临床病理特征与目标变量（BM的存在与否）之间的关联。该分析旨在深入了解可能影响SCLC患者BM风险的因素。本研究共纳入89366例SCLC患者，其中8269例（9.25%）发生BM。年龄、T分期、N分期、肝转移、肺转移、婚姻状况、收入、M分期、美国癌症联合委员会分期和脑转移被确定为SCLC的独立危险因素。在评估的各种预测模型中，利用XGB算法的机器学习模型在内部和外部数据验证中均表现出最高性能，训练集AUC得分：0.965，验证集AUC得分：0.962，测试集AUC得分：0.961。随后，利用XGB算法开发了一个用于SCLC患者BM的基于网络的预测工具。本研究开发了一种利用XGB算法的基于网络的预测工具来预测SCLC患者的BM风险，旨在为医生的临床决策提供有价值的帮助。
