Department of Neurosurgery, Daping Hospital, Army Medical University, Chongqing 400042, China.
Department of Neurosurgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China.
Comput Math Methods Med. 2022 Jul 20;2022:3436631. doi: 10.1155/2022/3436631. eCollection 2022.
To develop and authenticate a risk stratification framework and nomogram for ascertaining cancer-specific survival (CSS) among the pediatric brainstem gliomas.
For patients less than 12 years, according to Surveillance, Epidemiology, and End Results (SEER), information from 1998 to 2016 is found in their databases. The survival outcomes, treatments, and demographic clinicopathologic conditions are scrutinized per the database validation, and training cohorts are divided and validated using multivariate Cox regression analysis. A nomogram was designed, and predominantly, the risk stratification conceptualization engaged selected tenets according to the multivariate analysis. The model's authenticity was substantiated through C-index measure and calibration curves.
There are 806 pediatric concerns of histologically concluded brainstem glioma in the research. According to multivariate analysis, age, grade, radiotherapy, and race (with value < 0.05) depicted independent prognostic variations of the pediatric gliomas. The nomogram's C-index was approximately 0.75 and an accompanied predictive capability for CSS.
The nomogram constructed in this glioma's context is the primary predictor of using risk stratification. A combination of nomograms with the risk stratification mechanism assists clinicians in monitoring high-risk individuals and engage targeted accessory treatment.
开发并验证一种风险分层框架和列线图，以确定儿童脑干胶质瘤的癌症特异性生存（CSS）。
对于年龄小于 12 岁的患者，根据监测、流行病学和最终结果（SEER）数据库，在 1998 年至 2016 年的数据库中查找信息。根据数据库验证，对生存结果、治疗和人口统计学临床病理情况进行审查，并使用多变量 Cox 回归分析对训练队列进行分组和验证。设计了一个列线图，主要根据多变量分析采用选定的原则进行风险分层概念化。通过 C 指数测量和校准曲线来验证模型的真实性。
研究中涉及 806 例经组织学证实的儿童脑干胶质瘤病例。根据多变量分析，年龄、分级、放疗和种族（ 值<0.05）显示出儿童胶质瘤的独立预后差异。列线图的 C 指数约为 0.75，并且具有 CSS 的预测能力。
在这种胶质瘤背景下构建的列线图是风险分层的主要预测指标。列线图与风险分层机制的结合有助于临床医生监测高危人群并进行针对性的辅助治疗。
