Department of Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
Department of Cardiology, The First Hospital of Handan of Hebei Province, Handan, People's Republic of China.
Cancer Med. 2021 Sep;10(17):6140-6148. doi: 10.1002/cam4.4151. Epub 2021 Aug 3.
The prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5- and 10-year survival probability nomograms for patients with EPN.
Clinical data from the Surveillance, Epidemiology, and End Results (SEER) database were used for patients diagnosed with ependymoma between 2000 and 2018 and were randomized 7:3 into a development set and a validation set. Factors significantly associated with prognosis were screened out using the least absolute shrinkage and selection operator (LASSO) regression. The calibration chart and consistency index (C-index) are used to evaluate the discrimination and consistency of the prediction model. Decision curve analysis (DCA) was used to further evaluate the established model. Finally, prognostic factors selected by LASSO regression were evaluated using Kaplan-Meier (KM) survival curves.
A total of 3820 patients were included in the prognostic model. Seven survival predictors were obtained by LASSO regression screening, including age, gender, morphology, location, size, laterality, and resection. The prognostic model of the nomogram showed moderate discriminative ability in the development group and the validation group, with a C-index of 0.642 and 0.615, respectively. In the development set and validation set survival curves, the prognosis index of high risk was less effective than low risk (p < 0.001).
Our nomograms may play an important role in predicting 5 and 10-year outcomes for patients with ependymoma. This will help assist clinicians in personalized medicine.
影响室管膜瘤(EPN)患者生存的预后因素仍存在争议。本研究旨在建立 EPN 患者 5 年和 10 年生存概率列线图的预后模型。
本研究使用 2000 年至 2018 年期间监测、流行病学和最终结果(SEER)数据库中的临床数据,将患者随机分为 7:3 比例的开发组和验证组。使用最小绝对值收缩和选择算子(LASSO)回归筛选与预后显著相关的因素。校准图和一致性指数(C 指数)用于评估预测模型的区分度和一致性。决策曲线分析(DCA)用于进一步评估所建立的模型。最后,使用 Kaplan-Meier(KM)生存曲线评估 LASSO 回归选择的预后因素。
共纳入 3820 例患者用于预后模型。通过 LASSO 回归筛选得到 7 个生存预测因子,包括年龄、性别、形态、部位、大小、侧别和切除程度。列线图预后模型在开发组和验证组中均具有中等的区分能力,C 指数分别为 0.642 和 0.615。在开发组和验证组的生存曲线中,高风险预后指数的效果不如低风险(p<0.001)。
我们的列线图可用于预测 EPN 患者 5 年和 10 年的预后,这将有助于协助临床医生进行个性化医疗。