Fang Shu, Ren Guohua, Liu Qiuyue, Qiang Ling
Department of Breast Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.
The Fourth People's Hospital of Jinan, Jinan, Shandong, People's Republic of China.
Discov Oncol. 2025 Jun 23;16(1):1184. doi: 10.1007/s12672-025-02999-x.
The heterogeneity in outcomes of breast cancer liver metastasis (BCLM) complicates prognosis assessment. This study conducted conditional survival (CS) analysis and develop a CS-nomogram model for BCLM using SEER database data, providing individualized and adaptive prognostic predictions.
Data were extracted from the SEER 18 database, encompassing clinical records of BCLM patients diagnosed between 2010 and 2021. CS was calculated using the formula CS(t∣s) = S(t + s)/S(s), allowing for the dynamic assessment of survival probabilities. Annual hazard rate (AHR) analysis was performed to evaluate the risk of mortality at specific time intervals. A two-stage feature selection process was used to identify prognostic factors. We then developed a CS-nomogram, validated through calibration curves, time-dependent receiver operating characteristic curve (ROC) analysis, and decision curve analysis (DCA).
The study cohort comprised 4,702 BCLM patients. The CS analysis and AHR analysis demonstrated that survival probabilities improved progressively for patients who survived beyond the high-risk period, particularly during the first year post-diagnosis. The CS-nomogram, developed using Cox regression, incorporated 14 variables, including patient characteristics, tumor features, and treatment information. It effectively predicted overall survival and CS at 3, 5, and 10 years. The model's clinical utility was confirmed through calibrations, ROC with area under the curve values, and DCA, offering valuable insights for individualized treatment decisions.
By incorporating CS analysis, this study provided a dynamic, adaptable approach to predict prognosis for BCLMs. The CS-nomogram model transformed survival probabilities into a continuously adjustable process, supporting more precise clinical decision-making and offering hope to patients with a historically poor prognosis.
乳腺癌肝转移（BCLM）结局的异质性使预后评估变得复杂。本研究进行了条件生存（CS）分析，并使用监测、流行病学与最终结果（SEER）数据库数据开发了用于BCLM的CS列线图模型，以提供个性化和适应性的预后预测。
从SEER 18数据库中提取数据，涵盖2010年至2021年期间诊断的BCLM患者的临床记录。使用公式CS(t∣s) = S(t + s)/S(s)计算CS，从而实现对生存概率的动态评估。进行年度风险率（AHR）分析以评估特定时间间隔的死亡风险。采用两阶段特征选择过程来识别预后因素。然后我们开发了一个CS列线图，并通过校准曲线、时间依赖的受试者工作特征曲线（ROC）分析和决策曲线分析（DCA）进行验证。
研究队列包括4702例BCLM患者。CS分析和AHR分析表明，在高危期后存活的患者的生存概率逐渐提高，尤其是在诊断后的第一年。使用Cox回归开发的CS列线图纳入了14个变量，包括患者特征、肿瘤特征和治疗信息。它有效地预测了3年、5年和10年的总生存期和CS。通过校准、曲线下面积值的ROC和DCA证实了该模型的临床实用性，为个性化治疗决策提供了有价值的见解。
通过纳入CS分析，本研究提供了一种动态、适应性强的方法来预测BCLM的预后。CS列线图模型将生存概率转化为一个可连续调整的过程，支持更精确的临床决策，并为预后历来较差的患者带来希望。
