Department of Neurology, Beijing Pinggu Hospital, Beijing, China.
Department of Interventional Neuroradiology, Sanbo Brain Hospital, Capital Medical University, Beijing, China; Beijing Neurosurgical Institute, Capital Medical University, Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Beijing, China.
World Neurosurg. 2024 Sep;189:e391-e403. doi: 10.1016/j.wneu.2024.06.076. Epub 2024 Jun 21.
Anaplastic astrocytoma (AA) is an uncommon primary brain tumor with highly variable clinical outcomes. Our study aimed to develop practical tools for clinical decision-making in a population-based cohort study.
Data from 2997 patients diagnosed with AA between 2004 and 2015 were retrospectively extracted from the Surveillance, Epidemiology, and End Results database. The Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses were applied to select factors and establish prognostic nomograms. The discriminatory ability of these nomogram models was evaluated using the concordance index and receiver operating characteristic curve. Risk stratifications were established based on the nomograms.
Selected 2997 AA patients were distributed into the training cohort (70%, 2097) and the validation cohort (30%, 900). Age, household income, tumor site, extension, surgery, radiotherapy, and chemotherapy were identified as independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS). In the training cohort, our nomograms for OS and CSS exhibited good predictive accuracy with concordance index values of 0.752 (95% CI: 0.741-0.764) and 0.753 (95% CI: 0.741-0.765), respectively. Calibration and decision curve analyses curves showed that the nomograms demonstrated considerable consistency and satisfactory clinical utilities. With the establishment of nomograms, we stratified AA patients into high- and low-risk groups, and constructed risk stratification systems for OS and CSS.
We constructed two predictive nomograms and risk classification systems to effectively predict the OS and CSS rates in AA patients. These models were internally validated with considerable accuracy and reliability and might be helpful in future clinical practices.
间变性星形细胞瘤（AA）是一种罕见的原发性脑肿瘤，其临床结局差异较大。本研究旨在通过一项基于人群的队列研究，为临床决策制定提供实用工具。
回顾性提取 2004 年至 2015 年间从监测、流行病学和最终结果数据库诊断为 AA 的 2997 例患者的数据。应用最小绝对收缩和选择算子（LASSO）和多变量 Cox 回归分析选择因素并建立预后列线图。使用一致性指数和接收者操作特征曲线评估这些列线图模型的判别能力。根据列线图建立风险分层。
选定的 2997 例 AA 患者分为训练队列（70%，2097 例）和验证队列（30%，900 例）。年龄、家庭收入、肿瘤部位、扩展、手术、放疗和化疗被确定为总生存（OS）和癌症特异性生存（CSS）的独立预后因素。在训练队列中，我们的 OS 和 CSS 列线图具有良好的预测准确性，一致性指数值分别为 0.752（95%CI：0.741-0.764）和 0.753（95%CI：0.741-0.765）。校准和决策曲线分析曲线表明，该列线图具有相当的一致性和令人满意的临床实用性。通过建立列线图，我们将 AA 患者分为高风险和低风险组，并构建了 OS 和 CSS 的风险分层系统。
我们构建了两个预测列线图和风险分类系统，能够有效地预测 AA 患者的 OS 和 CSS 率。这些模型具有相当的准确性和可靠性，可用于未来的临床实践。
