Department of Radiology, Capital Medical University, Beijing Tiantan Hospital, No. 119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, P. R. China.
Department of Radiology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P. R. China.
J Radiat Res. 2024 May 23;65(3):350-359. doi: 10.1093/jrr/rrae007.
Using radiomics to predict O6-methylguanine-DNA methyltransferase promoter methylation status in patients with newly diagnosed glioblastoma and compare the performances of different MRI sequences. Preoperative MRI scans from 215 patients were included in this retrospective study. After image preprocessing and feature extraction, two kinds of machine-learning models were established and compared for their performances. One kind was established using all MRI sequences (T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient), and the other kind was based on single MRI sequence as listed above. For the machine-learning model based on all sequences, a total of seven radiomic features were selected with the Maximum Relevance and Minimum Redundancy algorithm. The predictive accuracy was 0.993 and 0.750 in the training and validation sets, respectively, and the area under curves were 1.000 and 0.754 in the two sets, respectively. For the machine-learning model based on single sequence, the numbers of selected features were 8, 10, 10, 13, 9, 7 and 6 for T1-weighted image, T2-weighted image, contrast enhancement, fluid-attenuated inversion recovery, DWI_b_high, DWI_b_low and apparent diffusion coefficient, respectively, with predictive accuracies of 0.797-1.000 and 0.583-0.694 in the training and validation sets, respectively, and the area under curves of 0.874-1.000 and 0.538-0.697 in the two sets, respectively. Specifically, T1-weighted image-based model performed best, while contrast enhancement-based model performed worst in the independent validation set. The machine-learning models based on seven different single MRI sequences performed differently in predicting O6-methylguanine-DNA methyltransferase status in glioblastoma, while the machine-learning model based on the combination of all sequences performed best.
使用放射组学预测新诊断胶质母细胞瘤患者 O6-甲基鸟嘌呤-DNA 甲基转移酶启动子甲基化状态，并比较不同 MRI 序列的性能。本回顾性研究纳入了 215 例患者的术前 MRI 扫描。在进行图像预处理和特征提取后，建立了两种机器学习模型并比较了它们的性能。一种模型使用所有 MRI 序列（T1 加权像、T2 加权像、对比增强、液体衰减反转恢复、DWI_b_high、DWI_b_low 和表观扩散系数），另一种模型基于上述单一 MRI 序列。对于基于所有序列的机器学习模型，使用最大相关性和最小冗余算法选择了总共 7 个放射组学特征。在训练集和验证集中，预测准确率分别为 0.993 和 0.750，曲线下面积分别为 1.000 和 0.754。对于基于单一序列的机器学习模型，T1 加权像、T2 加权像、对比增强、液体衰减反转恢复、DWI_b_high、DWI_b_low 和表观扩散系数分别选择了 8、10、10、13、9、7 和 6 个特征，在训练集和验证集中的预测准确率分别为 0.797-1.000 和 0.583-0.694，曲线下面积分别为 0.874-1.000 和 0.538-0.697。具体来说，基于 T1 加权像的模型在独立验证集中表现最好，而基于对比增强的模型表现最差。基于 7 种不同的单一 MRI 序列的机器学习模型在预测胶质母细胞瘤 O6-甲基鸟嘌呤-DNA 甲基转移酶状态方面表现不同，而基于所有序列组合的机器学习模型表现最好。
