PDF(Pubmed)
Abstract：
UNASSIGNED: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients.
UNASSIGNED: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves.
UNASSIGNED: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616-0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P < .001).
UNASSIGNED: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment.
UNASSIGNED: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves.
UNASSIGNED: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616-0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P < .001).
UNASSIGNED: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment.
摘要：
■复发性胶质瘤患者的总生存期（OS）很低。此外，放射性坏死对复发性胶质瘤的影响和预后因素仍存在争议.在这方面,开发有效的预测模型和指导临床护理对这些患者至关重要。
■我们筛选了放疗后复发的神经胶质瘤患者和在2013年8月1日至2020年12月31日期间接受手术的患者。单因素和多因素Cox回归分析确定了影响复发胶质瘤预后的独立预后因素。此外，列线图用于预测胶质瘤复发风险和预后。采用统计学方法确定基于曲线下面积(AUC)的列线图预测模型的预测准确性和可判别性,C指数，决策曲线分析(DCA)，和校准曲线。为了区分OS的高风险和低风险组，采用X-Tile和Kaplan-Meier(K-M)存活曲线，并通过X-Tile和K-M存活曲线进一步验证了列线图预测模型。
■根据Cox回归分析，放射性坏死放疗后复发胶质瘤的独立预后因素为世界卫生组织(WHO)分级和胶质增生百分率。我们利用列线图预测模型直观地分析结果。C指数为0.682(95%CI:0.616-0.748)。根据接收机工作特性(ROC)分析,校准图,和DCA,发现列线图预测模型具有高性能的能力，根据OS将所有患者分为低危组和高危组(P<.001)。
■WHO分级和胶质增生百分比是放射性坏死复发的神经胶质瘤的预后因素，建立了基于这两个变量的列线图预测模型。通过该模型可以将患者分为具有不同OS的高危组和低危组，它将为未来的治疗提供个性化的临床决策。
■我们筛选了放疗后复发的神经胶质瘤患者和在2013年8月1日至2020年12月31日期间接受手术的患者。单因素和多因素Cox回归分析确定了影响复发胶质瘤预后的独立预后因素。此外，列线图用于预测胶质瘤复发风险和预后。采用统计学方法确定基于曲线下面积(AUC)的列线图预测模型的预测准确性和可判别性,C指数，决策曲线分析(DCA)，和校准曲线。为了区分OS的高风险和低风险组，采用X-Tile和Kaplan-Meier(K-M)存活曲线，并通过X-Tile和K-M存活曲线进一步验证了列线图预测模型。
■根据Cox回归分析，放射性坏死放疗后复发胶质瘤的独立预后因素为世界卫生组织(WHO)分级和胶质增生百分率。我们利用列线图预测模型直观地分析结果。C指数为0.682(95%CI:0.616-0.748)。根据接收机工作特性(ROC)分析,校准图,和DCA,发现列线图预测模型具有高性能的能力，根据OS将所有患者分为低危组和高危组(P<.001)。
■WHO分级和胶质增生百分比是放射性坏死复发的神经胶质瘤的预后因素，建立了基于这两个变量的列线图预测模型。通过该模型可以将患者分为具有不同OS的高危组和低危组，它将为未来的治疗提供个性化的临床决策。
