Machine learning-based prediction of survival in breast cancer patients with lung metastasis: a SEER-based study.

IF 2.1 4区医学 Q4 ONCOLOGY

Translational cancer research Pub Date : 2026-08-31 Epub Date: 2026-07-16 DOI:10.21037/tcr-2026-1211

Cairong Wu, Guantong Liu, Haijie Xu, Hongguang Liu, Hansheng Wu

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引用次数: 0

Abstract

Background: Breast cancer remains the most prevalent malignancy among women, and breast cancer with lung metastasis (BCLM) significantly increases morbidity and mortality. Accurate prognosis prediction for BCLM patients is critical for guiding personalized treatment strategies, yet effective predictive tools remain limited. This study aimed to develop a machine learning (ML)-based predictive framework for BCLM prognosis by leveraging data from the Surveillance, Epidemiology, and End Results (SEER) database (2011-2020).

Methods: We analyzed data of 2,921 female BCLM patients from the SEER database (2011-2020). Advanced oversampling techniques (SMOTEENN, SMOTETomek) addressed class imbalance, and Bayesian optimization tuned model hyperparameters. Six ML classifiers-linear regression, elastic net, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM)-were evaluated. SHapley Additive exPlanations (SHAP) provided model interpretability and identified key prognostic factors.

Results: SVM achieved the highest performance in 1-year prediction [area under the curve (AUC): 0.770; 95% confidence interval (CI): 0.737-0.803], while XGBoost demonstrated slightly higher discrimination in 3-year (AUC: 0.770; 95% CI: 0.739-0.802) and 5-year (AUC: 0.746; 95% CI: 0.709-0.783) predictions. Key prognostic factors-marital status [specifically for the hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)- subtype] and metastatic sites (bone/liver)-were consistently identified as critical determinants of survival across models. SHAP analysis revealed interactions between sociodemographic and molecular factors.

Conclusions: The findings demonstrate that ML-based frameworks, particularly those integrating multimodal clinical data, can improve risk stratification for BCLM patients and provide actionable insights for personalized oncology. Limitations include reliance on retrospective registry data and unmeasured confounders; future research should incorporate longitudinal and imaging biomarkers to further refine predictive accuracy.

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基于机器学习的乳腺癌肺转移患者生存预测：一项基于seer的研究。

背景：乳腺癌仍然是女性中最常见的恶性肿瘤，乳腺癌伴肺转移（BCLM）显著增加了发病率和死亡率。准确预测BCLM患者的预后对于指导个性化治疗策略至关重要，但有效的预测工具仍然有限。本研究旨在利用监测、流行病学和最终结果（SEER）数据库（2011-2020）的数据，开发基于机器学习（ML）的BCLM预后预测框架。方法：我们分析了来自SEER数据库（2011-2020）的2921例女性BCLM患者的数据。先进的过采样技术（SMOTEENN, SMOTETomek）解决了类不平衡问题，贝叶斯优化调整了模型超参数。评估了六种ML分类器——线性回归、弹性网络、随机森林、支持向量机（SVM）、极端梯度增强（XGBoost）和梯度增强机（GBM）。SHapley加性解释（SHAP）提供了模型的可解释性并确定了关键的预后因素。结果：SVM在1年预测中表现最佳[曲线下面积（AUC）： 0.770；95%置信区间（CI）： 0.737-0.803]，而XGBoost在3年（AUC: 0.770; 95% CI: 0.739-0.802）和5年（AUC: 0.746; 95% CI: 0.709-0.783）预测中表现出略高的判别性。关键的预后因素——婚姻状况[特别是激素受体(HR)+/人表皮生长因子受体2 （HER2）亚型]和转移部位（骨/肝）——在所有模型中都被一致认定为生存的关键决定因素。SHAP分析揭示了社会人口学和分子因素之间的相互作用。结论：研究结果表明，基于ml的框架，特别是那些整合多模式临床数据的框架，可以改善BCLM患者的风险分层，并为个性化肿瘤学提供可操作的见解。局限性包括依赖于回顾性登记数据和未测量的混杂因素；未来的研究应纳入纵向和成像生物标志物，以进一步提高预测的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational cancer research ONCOLOGY-

CiteScore

2.10

自引率

0.00%

发文量

252

期刊介绍： Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.