背景:星形细胞瘤是一种成人型弥漫性神经胶质瘤,包括弥漫性星形细胞瘤(DA)和间变性星形细胞瘤(AA)。然而,使用基于人群的研究对DA和AA的风险评估和预后进行全面调查仍然非常缺乏.
方法:在本研究中,我们开发了两个预测列线图来评估与DA和AA相关的易感性和预后。该研究队列包括2010年至2019年期间从监测中选择的3,837名诊断为DA或AA的个体。流行病学,和结束结果(SEER)数据库。确定了独立的预测因子,并用于构建总体死亡(OD)和癌症特异性死亡(CSD)率的列线图。使用C指数评估模型的性能,校正曲线,和接收器工作特性曲线(ROC),临床适用性采用决策曲线分析(DCA)进行评价。
结果:本研究中的ROC曲线显示出出色的临床适用性和预测能力。值得注意的是,训练队列和验证队列的曲线下面积(AUC)大于0.80,从而巩固了模型的精度.此外,校准图表明,预期死亡率与测量值非常吻合.这种数字对齐在验证队列中是持续的。此外,决策曲线分析(DCA)证实了模型的平移潜力,加强它们在现实世界临床环境中的相关性。
结论:所呈现的列线图不仅表现出良好的预测性能,而且还展示了在预测患者预后方面的实用临床效用。重要的是,这无疑将成为肿瘤学家的宝贵资产,促进知情的治疗决定,并促进细致的后续计划。
BACKGROUND: Astrocytoma is a type of adult-type diffuse gliomas that includes diffuse astrocytoma (DA) and anaplastic astrocytoma (AA). However, comprehensive investigations into the risk assessment and prognosis of DA and AA using population-based studies remain noticeably scarce.
METHODS: In this study, we developed 2 predictive nomograms to evaluate the susceptibility and prognosis associated with DA and AA. The study cohort comprised 3837 individuals diagnosed with DA or AA between 2010 and 2019 selected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent predictors were identified and used to construct the nomograms for overall death and cancer-specific death rates. The performance of the models was assessed using C-index, calibration curves, and receiver operating characteristic curve, and the clinical applicability was evaluated using decision curve analysis.
RESULTS: The receiver operating characteristic curves in this study show excellent clinical applicability and predictive power. Notably, the area under the curves of the training and verification queues was higher than 0.80, thereby cementing the models\' precision. Additionally, the calibration plots demonstrate that the anticipated mortality rates strikingly match the measured values. This alignment of figures is sustained in the validation cohort. Furthermore, the decision curve analysis corroborates the models\' translational potential, reinforcing their relevance within real-world clinical settings.
CONCLUSIONS: The presented nomograms have not only exhibited good predictive performance but also showcased pragmatic clinical utility in prognosticating patient outcomes. Significantly, this will undoubtedly serve as a valuable asset for oncologists, facilitating informed treatment decisions and meticulous follow-up planning.