Abstract：
OBJECTIVE: Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients\' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features.
METHODS: Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria.
RESULTS: One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% ( P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set.
CONCLUSIONS: Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.
METHODS: Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria.
RESULTS: One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% ( P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set.
CONCLUSIONS: Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.
摘要：
目的：少突胶质细胞转录因子2（OLIG2）在人胶质母细胞瘤（GB）中普遍表达。我们的研究探讨了OLIG2表达是否影响GB患者的总体生存，并建立了基于临床的GB患者OLIG2水平预测的机器学习模型，语义，和磁共振成像影像学特征。
方法：Kaplan-Meier分析用于确定168例GB患者中OLIG2的最佳截止值。纳入OLIG2预测模型的13名患者以7：3的比例随机分为训练集和测试集。放射学，语义，并收集每位患者的临床特征.递归特征消除（RFE）用于特征选择。随机森林(RF)模型的建立和微调，并计算曲线下面积以评估性能。最后,使用第5版中枢神经系统肿瘤分类标准,构建了排除IDH突变患者的新测试集,并在预测模型中进行了测试.
结果：共有119例患者被纳入生存分析。少突胶质细胞转录因子2与GB生存呈正相关,最佳临界值为10%(P=0.00093)。一百三十四名患者符合OLIG2预测模型的条件。基于2个语义和21个放射学签名的RFE-RF模型在训练集中获得了0.854的曲线下面积，测试集中的0.819，和0.825在新的测试集。
结论：OLIG2表达≤10%的胶质母细胞瘤患者的总生存期更差。集成23个特征的RFE-RF模型可以预测术前GB患者的OLIG2水平，无论中枢神经系统分类标准如何，进一步指导个体化治疗。
方法：Kaplan-Meier分析用于确定168例GB患者中OLIG2的最佳截止值。纳入OLIG2预测模型的13名患者以7：3的比例随机分为训练集和测试集。放射学，语义，并收集每位患者的临床特征.递归特征消除（RFE）用于特征选择。随机森林(RF)模型的建立和微调，并计算曲线下面积以评估性能。最后,使用第5版中枢神经系统肿瘤分类标准,构建了排除IDH突变患者的新测试集,并在预测模型中进行了测试.
结果：共有119例患者被纳入生存分析。少突胶质细胞转录因子2与GB生存呈正相关,最佳临界值为10%(P=0.00093)。一百三十四名患者符合OLIG2预测模型的条件。基于2个语义和21个放射学签名的RFE-RF模型在训练集中获得了0.854的曲线下面积，测试集中的0.819，和0.825在新的测试集。
结论：OLIG2表达≤10%的胶质母细胞瘤患者的总生存期更差。集成23个特征的RFE-RF模型可以预测术前GB患者的OLIG2水平，无论中枢神经系统分类标准如何，进一步指导个体化治疗。
