College of Medicine, Sulaiman Al Rajhi Colleges, Al Bukayriyah, Saudi Arabia.
Faculty of Medicine, Mansoura University, Mansoura, Egypt.
J Neurosurg Sci. 2023 Feb;67(1):93-102. doi: 10.23736/S0390-5616.20.05033-X. Epub 2020 Sep 24.
Malignant ependymomas are rare cancerous tumors that are associated with increased morbidity and mortality in the affected patients. Lately, there has been a lot of controversy about the correct way to manage and predict the survival outcome of these patients. We aim in this retrospective cohort study to develop novel nomograms that can better predict the overall survival (OS) and cancer-specific survival (CSS) of these patients.
This is a retrospective cohort study that was conducted through the Surveillance, Epidemiology, and End Results databases (SEER) between 1998 and 2016. Patients were excluded if they had an unknown diagnosis, unknown cause of death or those with survival duration less than a month. We used penalized regression models with the highest time-dependent area under the ROC curve (AUC) and most stable calibrations to construct the nomograms. By searching the SEER database and applying the eligibility criteria, we identified 3391 patients for the final analysis.
Nine penalized regression models were developed of which two models including adaptive elastic-net was selected for both OS and CSS. The model incorporated age, sex, year of diagnosis, site, race, radiation, chemotherapy, surgery, and type for the construction of nomograms. We aimed in this population-based cohort study to develop novel prediction tools that can help physicians estimate the survival of malignant ependymoma patients and provide better care.
Our nomograms appear to have high accuracy and applicability, which we hope that can predict the survival and improve the treatment and prognosis of these patients.
恶性室管膜瘤是一种罕见的癌性肿瘤，会增加患者的发病率和死亡率。最近，对于如何正确管理和预测这些患者的生存结果存在很多争议。我们旨在这项回顾性队列研究中开发新的列线图，以更好地预测这些患者的总生存（OS）和癌症特异性生存（CSS）。
这是一项回顾性队列研究，通过 1998 年至 2016 年的监测、流行病学和最终结果（SEER）数据库进行。如果患者的诊断未知、死因未知或生存时间少于一个月，则将其排除在外。我们使用具有最高时间依赖性 ROC 曲线下面积（AUC）和最稳定校准的惩罚回归模型来构建列线图。通过搜索 SEER 数据库并应用入选标准，我们确定了 3391 名患者进行最终分析。
共开发了 9 个惩罚回归模型，其中包括自适应弹性网络的两个模型，用于 OS 和 CSS。该模型纳入了年龄、性别、诊断年份、部位、种族、放疗、化疗、手术和类型等因素来构建列线图。我们旨在这项基于人群的队列研究中开发新的预测工具，以帮助医生估计恶性室管膜瘤患者的生存情况并提供更好的护理。
我们的列线图似乎具有较高的准确性和适用性，希望能够预测患者的生存情况，并改善这些患者的治疗和预后。
