Liu Jingxin, Wang Yibing, Zhou Xianwei, Reng Meijin, Xiang Ziyue, Chang Ruimin, Hao Wen, Sun Xitai, Yang Yang
Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Department of Oncology, Nanjing Drum Tower Hospital, Drum Tower Hospital Clinical College, Nanjing University of Chinese Medicine, Nanjing, China.
Transl Cancer Res. 2025 Aug 31;14(8):4638-4648. doi: 10.21037/tcr-2025-131. Epub 2025 Aug 27.
Brain metastasis, the most prevalent site of lung cancer metastasis, implies a grim prognosis. Adopting the best treatment approach is crucial for improving the survival of these patients. Therefore, this study aimed to develop a personalized prognostic model for brain-metastasized non-small cell lung cancer (BM-NSCLC) patients to aid in clinical decision-making.
The study enrolled BM-NSCLC patients who were single-primary and had not undergone radical surgery from 2010 to 2021. The Kaplan-Meier method analysis was utilized to assess overall survival (OS) and cancer-specific survival (CSS) under different treatments. Univariable and multivariable Cox regression analyses were conducted to ascertain independent prognostic factors. The dataset was partitioned into training (70%) and validation (30%) cohorts for the development and assessment of random forest (RF), logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) models. The efficacy of the models was evaluated through the calculation of area under the curve (AUC) of the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). A user-friendly web app was developed via shinyapps.io to increase the accessibility for clinicians.
A total of 3,171 eligible samples were ultimately included in the study. Survival analysis indicated that patients who underwent metastasis site surgery combined with radiotherapy based on chemotherapy exhibited a more favorable prognosis compared to alternative treatment modalities within the scope of this study. The RF model demonstrated superior predictive accuracy for 1-year-OS, with an AUC of 0.89 in validation cohorts (n=951), and a more refined DCA profile.
In the case of patients with BM-NSCLC, the integration of radiation therapy with surgery for metastasis site based on systematic treatment yielded the most significant benefits. The importance of a comprehensive treatment strategy that integrates chemotherapy, surgery, and radiotherapy for these patients was emphasized. Additionally, a clinical decision-support tool constructed from this dataset, demonstrated robust discrimination, excellent calibration, and notable clinical utility. This tool will effectively assist clinical practitioners in making more personalized clinical decisions for patients.
脑转移是肺癌转移最常见的部位，预后较差。采用最佳治疗方法对于提高这些患者的生存率至关重要。因此，本研究旨在为脑转移非小细胞肺癌（BM-NSCLC）患者建立个性化预后模型，以辅助临床决策。
本研究纳入了2010年至2021年期间的单原发性且未接受根治性手术的BM-NSCLC患者。采用Kaplan-Meier法分析评估不同治疗方案下的总生存期（OS）和癌症特异性生存期（CSS）。进行单变量和多变量Cox回归分析以确定独立预后因素。将数据集分为训练集（70%）和验证集（30%）队列，用于开发和评估随机森林（RF）、逻辑回归（LR）、支持向量机（SVM）和K近邻（KNN）模型。通过计算受试者操作特征（ROC）曲线下面积（AUC）和决策曲线分析（DCA）评估模型的效能。通过shinyapps.io开发了一个用户友好的网络应用程序，以提高临床医生的可及性。
本研究最终共纳入3171例合格样本。生存分析表明，在本研究范围内，与其他治疗方式相比，接受转移部位手术联合基于化疗的放疗的患者预后更佳。RF模型在1年OS方面表现出卓越的预测准确性，验证队列（n=951）中的AUC为0.89，且DCA曲线更为精细。
对于BM-NSCLC患者，基于系统治疗的转移部位手术联合放疗带来的获益最为显著。强调了针对这些患者综合化疗、手术和放疗的综合治疗策略的重要性。此外，基于该数据集构建的临床决策支持工具具有强大的辨别力、良好的校准度和显著的临床实用性。该工具将有效协助临床医生为患者做出更个性化的临床决策。
