Sun Chenjun, Yang Zhihao, Gu Zhiwei, Huang Hua
Department of Neurosurgery, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, China.
Discov Oncol. 2024 Sep 18;15(1):460. doi: 10.1007/s12672-024-01275-8.
Traditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients.
Data on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified.
In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1-9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated.
This study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.
传统生存分析常用于评估室管膜瘤（EPN）的预后；然而，对于已存活数年的患者，它可能无法提供更多的生存见解。因此，这种疾病的条件生存（CS）模式尚有待进一步研究。本研究旨在使用CS分析评估随时间推移的生存改善情况，并开发基于CS的列线图模型，用于EPN患者的实时动态生存估计。
从监测、流行病学和最终结果（SEER）数据库收集EPN患者的数据。为了有效地构建和验证模型，将选定的患者按7:3的比例随机分组。CS定义为患者在初始诊断后存活x年的情况下，在特定时间段（y年）内存活的概率。探索了EPN患者的CS模式。然后，采用具有十折交叉验证的最小绝对收缩和选择算子（LASSO）回归方法来识别预后预测因素。采用多变量Cox回归开发基于CS的列线图模型，并使用该模型对EPN患者的风险进行量化。最后，对预测模型的性能进行了评估和验证。
本研究共纳入1829例诊断为EPN的患者，训练队列和验证队列分别有1280例和549例患者。CS分析表明，患者的总生存期随时间逐渐改善。诊断后每多存活一年，EPN患者的10年生存率就会提高，从最初的74%分别提高到79%、82%、85%、87%、89%、91%、93%、96%和98%（分别在存活1 - 9年后）。实施十折交叉验证的LASSO回归模型确定了7个显著预测因素（年龄、肿瘤分级、肿瘤部位、肿瘤扩展、肿瘤大小、手术和放疗），以开发基于CS的列线图模型。并基于列线图模型对这些患者进行了进一步的风险分层。此外，该生存预测模型得到了成功验证。
本研究描述了EPN患者的CS模式，并强调了长期幸存者随时间推移观察到的生存逐渐改善情况。我们还开发了首个新型CS列线图模型，能够进行个性化和实时预后预测。但必须告知患者，个体情况可能并不总是准确反映列线图的结果。
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