Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. Results: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p= 0.004), age (p= 0.039), andMGMTstatus (p= 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. Conclusions: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, andMGMTstatus can predict survival ≥ 18 months in patients with GBM.
背景：临床、组织病理学和影像学变量与胶质母细胞瘤（GBM）患者的预后相关。本研究旨在开发一种结合MRI纹理特征、人口统计学数据及组织病理学肿瘤生物标志物的多参数放射基因组学模型，以预测GBM患者的预后。方法：在这项回顾性研究中，纳入经组织病理学生物标志物确诊且具有术前MRI的GBM患者。通过肿瘤分割提取纹理特征，并采用多变量分析和最小绝对收缩与选择算子（LASSO）正则化方法构建预测生存期（<18个月 vs. ≥18个月）的放射组学模型，以降低过拟合风险。将该放射组学模型与临床及组织病理学数据共同纳入反向逐步逻辑回归模型进行生存期评估，并在训练集和外部验证集中报告该模型的诊断性能。结果：共纳入116例患者用于模型开发，40例患者用于外部测试验证。基于七个纹理特征构建的放射组学模型在预测≥18个月生存期方面的诊断性能（AUC/灵敏度/特异度）为0.71/69.0/70.3。最终确定三个独立预测生存期的变量：放射组学特征（p=0.004）、年龄（p=0.039）和MGMT状态（p=0.025）。该模型在预测≥18个月生存期时，训练集的诊断性能（AUC/灵敏度/特异度）为0.77/81.0/66.0，测试集为0.89/100/78.6。结论：研究结果表明，基于基线MRI放射组学特征、年龄和MGMT状态构建的放射基因组学模型能够有效预测GBM患者≥18个月的生存期。
Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma
肿瘤(癌症)患者之家