Li Chaofan, Wang Yusheng, Fang Biyun, Liu Mengjie, Sun Shiyu, Qu Jingkun, Zhang Shuqun, Du Chong
The Comprehensive Breast Care Center, The Second Affiliated Hospital of Xi'an Jiaotong University, 157 West Fifth Street, Xi'an, Shaanxi, PR China.
Department of Otolaryngology, The Second Affiliated Hospital of Xi'an Jiaotong University, 157 West Fifth Street, Xi'an, Shaanxi, PR China.
Breast. 2025 Aug;82:104483. doi: 10.1016/j.breast.2025.104483. Epub 2025 Apr 23.
Although meta-analyses have demonstrated survival benefits associated with primary tumor resection in MBC, guidelines lack consensus on the survival benefit of postoperative radiation therapy (RT).
In this study, we included 1392 patients with de novo metastatic breast cancer (dnMBC) by integrating data from the SEER database (2010-2019) to systematically assess the efficacy of postoperative RT and develop a machine learning-driven prognostic tool. The primary endpoint was overall survival (OS).
Propensity score matching (PSM) results showed that postoperative RT significantly improved OS (HR = 0.573, 95 % CI = 0.475-0.693), but this survival gain showed great heterogeneity among different subgroups. It is found that patients with HR-/HER2-or HR+/HER2-subtypes gained significant OS benefit from (p < 0.001) postoperative RT, whereas patients with HER2+ subtype did not gain any survival benefit since the effect of targeted therapy overshadowed the postoperative RT. Further risk stratification by the random survival forest (RSF) model revealed that high-risk patients with T4/N3 stage, high tumor grade and poor response to chemotherapy had significantly prolonged OS after receiving RT (p < 0.001), while low-risk patients showed no additional benefit. The model had excellent predictive efficacy (training set C-index = 0.741, validation set C-index = 0.720) with key predictors including HER2 status, chemotherapy response and tumor grade. The research team developed an interactive web application (https://lee2287171854.shinyapps.io/RSFshiny/) based on this model, which can generate individualized survival risk scores in real-time to guide clinical decision-making.
This study is the first to propose a risk stratification strategy for postoperative RT in dnMBC, and innovatively integrates machine learning and clinical tools to provide a new paradigm for optimizing precision therapy.
尽管荟萃分析已证明原发性肿瘤切除对转移性乳腺癌（MBC）患者的生存有益，但关于术后放疗（RT）的生存获益，指南尚未达成共识。
在本研究中，我们整合了监测、流行病学和最终结果（SEER）数据库（2010 - 2019年）的数据，纳入1392例初发性转移性乳腺癌（dnMBC）患者，以系统评估术后放疗的疗效，并开发一种机器学习驱动的预后工具。主要终点为总生存期（OS）。
倾向评分匹配（PSM）结果显示，术后放疗显著改善了总生存期（HR = 0.573，95%CI = 0.475 - 0.693），但这种生存获益在不同亚组之间存在很大异质性。研究发现，激素受体阴性/人表皮生长因子受体2阴性（HR-/HER2-）或激素受体阳性/人表皮生长因子受体2阴性（HR+/HER2-）亚型的患者从术后放疗中获得了显著的总生存期获益（p < 0.001），而人表皮生长因子受体2阳性（HER2+）亚型的患者未获得任何生存获益，因为靶向治疗的效果超过了术后放疗。通过随机生存森林（RSF）模型进行的进一步风险分层显示，T4/N3期、高肿瘤分级且化疗反应不佳的高危患者在接受放疗后总生存期显著延长（p < 0.001），而低危患者未显示出额外获益。该模型具有出色的预测效能（训练集C指数 = 0.741，验证集C指数 = 0.720），关键预测因素包括HER2状态、化疗反应和肿瘤分级。研究团队基于该模型开发了一个交互式网络应用程序（https://lee2287171854.shinyapps.io/RSFshiny/），可实时生成个性化的生存风险评分，以指导临床决策。
本研究首次提出了dnMBC术后放疗的风险分层策略，并创新性地将机器学习与临床工具相结合，为优化精准治疗提供了新的范例。
