Meng Xiangdi, Wang Peihe, Liu Jie, Sun Daqing, Ju Zhuojun, Cai Yuanyuan
Department of Radiation Oncology, Weifang People's Hospital, Weifang, China.
Department of Radiation Oncology, Gunma University Graduate School of Medicine, Maebashi, Japan.
Front Med (Lausanne). 2025 Jan 7;11:1491337. doi: 10.3389/fmed.2024.1491337. eCollection 2024.
Conditional survival (CS) analysis can estimate further survival probabilities based on the time already survived, providing dynamic updates for prognostic information. This study aimed to develop a CS-nomogram to promote individualized disease management for stage III non-small cell lung cancer (NSCLC).
This study included patients diagnosed with stage III NSCLC in the Surveillance, Epidemiology, and End Results database from 2010 to 2017 ( = 3,512). The CS was calculated as CS(y|x) = OS(y + x)/OS(x), where OS(y + x) and OS(x) were the overall survival (OS) in the year (y + x) and year x, respectively, calculated by the Kaplan-Meier method. We used the least absolute shrinkage and selection operator (LASSO) regression to identify predictors and developed the CS-nomogram based on these predictors and the CS formula.
The CS analysis provided real-time updates on survival, with 5-year OS improving dynamically from 14.4 to 29.9%, 47.9, 66.0, and 80.8% (after 1-4 years of survival). Six independent predictors (age, tumor size, N status, surgery, radiotherapy and chemotherapy) were identified for the development of the CS-nomogram and its web version (https://dynapp.shinyapps.io/NSCLC/). The model performed with an excellent concordance index (C-index) of 0.71 (95% CI: 0.70-0.72), and a median time-dependent AUC of 0.71-0.73 from 200 iterations 5-fold cross-validation.
The study demonstrated the improvement in real-time OS over time in stage III NSCLC survivors and developed the novel CS-nomogram to provide patients with updated survival data. It provided novel insights into clinical decisions in follow-up and treatment for survivors, offering a convenient tool for optimize resource allocation.
条件生存（CS）分析可根据已存活时间估计进一步的生存概率，为预后信息提供动态更新。本研究旨在开发一种CS列线图，以促进III期非小细胞肺癌（NSCLC）的个体化疾病管理。
本研究纳入了2010年至2017年监测、流行病学和最终结果数据库中诊断为III期NSCLC的患者（n = 3512）。CS计算为CS(y|x) = OS(y + x)/OS(x)，其中OS(y + x)和OS(x)分别是通过Kaplan-Meier方法计算的第(y + x)年和第x年的总生存（OS）。我们使用最小绝对收缩和选择算子（LASSO）回归来识别预测因素，并基于这些预测因素和CS公式开发了CS列线图。
CS分析提供了生存的实时更新，5年总生存率从14.4%动态提高到29.9%、47.9%、66.0%和80.8%（存活1 - 4年后）。确定了六个独立预测因素（年龄、肿瘤大小、N分期、手术、放疗和化疗）用于开发CS列线图及其网络版本（https://dynapp.shinyapps.io/NSCLC/）。该模型的一致性指数（C指数）为0.71（95%CI：0.70 - 0.72），在200次迭代5折交叉验证中，中位时间依赖性AUC为0.71 - 0.73。
该研究证明了III期NSCLC幸存者的实时总生存率随时间的改善，并开发了新型CS列线图为患者提供更新的生存数据。它为幸存者的随访和治疗临床决策提供了新见解，为优化资源分配提供了便利工具。
