Zhang Rufei, Chen Xiaodan, Cai Jialing, Jiang Peirong, Chen Yilin, Sun Bin, Song Yang, Lin Lin, Xue Yunjing
Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China.
School of Medical Technology and Engineering, Fujian Medical University, Fuzhou, China.
Front Oncol. 2021 Oct 19;11:737520. doi: 10.3389/fonc.2021.737520. eCollection 2021.
Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.
脑膜瘤的病理分级不足以预测切除术后的复发情况及指导个体化治疗策略。本回顾性研究纳入了133例行全切除的脑膜瘤患者。进行单因素分析以评估各因素与复发之间的关联。使用最小绝对收缩和选择算子（Lasso）进一步选择变量以构建逻辑模型。通过受试者工作特征曲线（ROC）、决策曲线分析（DCA）和净重新分类改善（NRI）比较模型与世界卫生组织（WHO）分级的预测效率。根据列线图为患者赋予新的风险分层。通过Kaplan-Meier曲线观察不同组中脑膜瘤的复发情况。单因素分析表明11个危险因素与预后相关（P<0.05）。ROC结果证明，量化风险评分系统（AUC = 0.853）比病理分级（AUC = 0.689，P = 0.011）具有更高的预测效能。通过Kaplan-Meier分析，高风险队列的复发率（69%）显著高于低风险队列（9%）（P<0.001）。并且高风险组中所有未复发的患者均接受了辅助放疗。这种新的风险分层算法对脑膜瘤的复发具有重要价值，有助于优化临床治疗的个体化设计。
