Background/Objectives: Breast cancer is the most common cancer in women worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a predictive classification model for breast cancer mortality using real-world data, including various clinical features.Methods: A total of 11,286 patients with breast cancer from the National Cancer Center were included in this study. The mortality rate of the total sample was approximately 6.2%. Propensity score matching was used to reduce bias. Several machine learning models, including extreme gradient boosting, were applied to 31 clinical features. To enhance model interpretability, we used the SHapley Additive exPlanations method. ML analyses were also performed on the samples, excluding patients who developed other cancers after breast cancer.Results: Among the ML models, the XGB model exhibited the highest discriminatory power, with an area under the curve of 0.8722 and a specificity of 0.9472. Key predictors of the mortality classification model included occurrence in other organs, age at diagnosis, N stage, T stage, curative radiation treatment, and Ki-67(%). Even after excluding patients who developed other cancers after breast cancer, the XGB model remained the best-performing, with an AUC of 0.8518 and a specificity of 0.9766. Additionally, the top predictors from SHAP were similar to the results for the overall sample.Conclusions: Our models provided excellent predictions of breast cancer mortality using real-world data from South Korea. Explainable artificial intelligence, such as SHAP, validated the clinical applicability and interpretability of these models.
背景/目的：乳腺癌是全球女性最常见的恶性肿瘤，降低其死亡率需要采取系统性干预策略。本研究旨在利用包含多维度临床特征的真实世界数据，构建乳腺癌死亡风险的预测分类模型。 方法：本研究纳入国家癌症中心11,286例乳腺癌患者，总体死亡率约为6.2%。采用倾向性评分匹配法控制混杂偏倚，基于31项临床特征构建包括极端梯度提升在内的多种机器学习模型。为增强模型可解释性，应用SHAP（沙普利加性解释）方法进行特征解析。同时针对排除乳腺癌后继发其他恶性肿瘤的亚组样本进行机器学习分析。 结果：在各类机器学习模型中，XGB模型展现出最优判别效能，其曲线下面积达0.8722，特异性为0.9472。死亡风险分类模型的关键预测因子包括：其他器官转移情况、确诊年龄、N分期、T分期、根治性放疗实施情况及Ki-67（%）表达水平。即使在排除继发其他恶性肿瘤患者后，XGB模型仍保持最佳性能（AUC=0.8518，特异性=0.9766），且SHAP方法识别的核心预测因子与全样本分析结果高度一致。 结论：本研究基于韩国真实世界数据构建的模型能有效预测乳腺癌死亡风险。以SHAP为代表的可解释人工智能技术验证了该模型具备良好的临床适用性与可解释性。
Mortality Prediction Modeling for Patients with Breast Cancer Based on Explainable Machine Learning
肿瘤(癌症)患者之家