Wang Bowen, Peng Mengjia, Li Yan, Gao Jinhang, Chang Tao
Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.
Department of Emergency, General Hospital of Tibet Military Command, Lhasa, China.
Front Oncol. 2025 Mar 3;15:1554242. doi: 10.3389/fonc.2025.1554242. eCollection 2025.
Primary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.
The records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.
The research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model's performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.
BM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition.
原发性肺癌（LCs）常转移至脑，导致受影响个体预后不佳。这项基于人群的研究旨在调查其生存期和免疫状态，同时建立一个预测模型。
从监测、流行病学和最终结果（SEER）数据库中提取86763例原发性LCs的记录，其中包括15180例有脑转移（BM）的病例和71583例无BM的病例。采用单因素和多因素Cox回归构建预测模型。应用多种机器学习方法对模型进行验证。采用流式细胞术和酶联免疫吸附测定（ELISA）在真实队列中探索免疫状态。
研究结果显示，LCs发生BM的患病率为17.49%，中位生存期为8个月，而无BM的患者为16个月（P<0.001）。基于这些变量开发了一个列线图，以预测1年、3年和5年的生存率，其时间依赖性曲线下面积（AUC）分别为0.857、0.814和0.786。此外，几种机器学习方法进一步验证了该模型性能的可靠性。流式细胞术和ELISA分析表明，预测模型与免疫状态相关。
LCs发生BM的预后较差。考虑到这些因素的重大影响，列线图模型是指导此类患者临床决策的有价值工具。
