Ren Xiaoyu, Cong Fei, Chao Gao, Yang Cheng, Guo Yunshan, Fan Jinzhu
Department of Bone Microsurgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.
Department of Spinal Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.
Transl Cancer Res. 2025 Mar 30;14(3):1710-1724. doi: 10.21037/tcr-24-1912. Epub 2025 Mar 17.
Unlike traditional survival analysis methods, conditional survival (CS) provides enhanced insight by offering a personalized prognosis estimation as time advances for tumor patients. This study aimed to estimate CS and devised a novel CS-nomogram for real-time prediction of 10-year CS for patients with spinal chordoma.
Patients diagnosed with spinal chordoma from 2000 to 2019, as documented in the Surveillance, Epidemiology, and End Results (SEER) database, were included in this study. CS represents the likelihood of surviving an additional y years given that the patient has already survived x years. It is computed using the equation CS(x|y) = S(x + y)/S(x), where S(x) denotes the patient's survival rate at x years. The univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO) analysis and best subset regression (BSR) methods were employed for variable selection. Based on these selected factors, the CS-based nomogram and a risk classification system were developed. Finally, several approaches were used to validate the performance of our model.
Between 2000 and 2019, the SEER database identified 730 patients with spinal chordoma, distributed into 510 in the training group and 220 in the validation group. CS analysis showed that patients experienced a gradual augmentation in their 10-year survival rates over the course of each additional year post-diagnosis. We also successfully created a CS-based nomogram model for forecasting 3-, 5-, and 10-year overall survival, along with 10-year CS. The CS-based nomogram incorporating age, tumor size, tumor extension, multiple primary tumors, and surgery demonstrated robust predictive capabilities. Moreover, a novel risk classification system was constructed to aid in tailored management strategies and personalized treatment decisions for spinal chordoma patients.
In contrast to traditional survival assessment methods, our analysis of CS yielded more dynamic and real-time outcomes for spinal chordoma patients. Via our CS-based nomogram model and risk classification system, we have provided more precise prognostic insights for these patients, aiding in treatment planning and follow-up strategy formulation in clinical settings.
与传统生存分析方法不同，条件生存（CS）通过随着时间推移为肿瘤患者提供个性化预后估计，从而提供了更深入的见解。本研究旨在估计CS，并设计一种新型的CS列线图，用于实时预测脊索瘤患者的10年条件生存情况。
本研究纳入了2000年至2019年在监测、流行病学和最终结果（SEER）数据库中记录的诊断为脊索瘤的患者。CS表示患者已经存活x年后再存活y年的可能性。它使用公式CS(x|y) = S(x + y)/S(x)计算，其中S(x)表示患者在x年时的生存率。采用单变量Cox风险回归、最小绝对收缩和选择算子（LASSO）分析以及最佳子集回归（BSR）方法进行变量选择。基于这些选定因素，开发了基于CS的列线图和风险分类系统。最后，使用多种方法验证了我们模型的性能。
2000年至2019年期间，SEER数据库识别出730例脊索瘤患者，其中510例分配到训练组，220例分配到验证组。CS分析表明，患者在诊断后的每一年中，其10年生存率逐渐提高。我们还成功创建了一个基于CS的列线图模型，用于预测3年、5年和10年总生存率以及10年CS。纳入年龄、肿瘤大小、肿瘤扩展、多原发性肿瘤和手术情况的基于CS的列线图显示出强大的预测能力。此外，构建了一种新型风险分类系统，以帮助制定针对脊索瘤患者的定制管理策略和个性化治疗决策。
与传统生存评估方法相比，我们对CS的分析为脊索瘤患者产生了更动态和实时的结果。通过我们基于CS的列线图模型和风险分类系统，我们为这些患者提供了更精确的预后见解，有助于临床环境中的治疗计划和随访策略制定。
