PDF(Pubmed)
Abstract：
Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients\' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient\'s overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients\' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies.
摘要：
多形性胶质母细胞瘤（GBM）是最常见和侵袭性的原发性脑肿瘤。尽管基于替莫唑胺（TMZ）的放化疗可改善GBM患者的总体生存率，它还增加了治疗后磁共振成像(MRI)评估肿瘤进展的假阳性频率.假性进展（PsP）是一种与治疗相关的反应，在MRI上，肿瘤部位或切除边缘的对比增强病变大小增加，影响肿瘤复发。在GBM患者的临床管理中，迫切需要准确可靠地预测GBM进展。临床资料分析表明，PsP患者的总体生存率和无进展生存率均较高。在这项研究中,我们旨在建立一个预后模型,以评估GBM患者接受标准治疗后的肿瘤进展潜能.我们应用字典学习方案从Wake数据集中获得具有PsP或真实肿瘤进展（TTP）的GBM患者的成像特征。基于这些射线照相特征,我们进行了放射基因组学分析,以鉴定显著相关的基因.这些显著相关的基因被用作构建2YS(2年生存率)逻辑回归模型的特征。根据从该模型得到的个体2YS评分将GBM患者分为低生存风险组和高生存风险组。我们使用独立的癌症基因组图谱计划（TCGA）数据集测试了我们的模型，发现2YS评分与患者的总生存期显着相关。我们使用了两组TCGA数据来训练和测试我们的模型。我们的结果表明，来自训练和测试TCGA数据集的基于2YS分数的分类结果与患者的总体生存率显着相关。我们还分析了其他临床因素(性别，年龄,KPS（Karnofsky性能状态），正常细胞比率），并发现这些因素与患者的生存无关或弱相关。总的来说,我们的研究证明了2YS模型在预测GBM患者接受标准治疗后的临床结局方面的有效性和稳健性.
