Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 15th Floor, No. 172-1, Keelung Rd., Sect. 2, Da-an District, Taipei, 106, Taiwan, ROC.
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC.
Sci Rep. 2022 Aug 4;12(1):13412. doi: 10.1038/s41598-022-17707-w.
O6-Methylguanine-DNA-methyltransferase (MGMT) promoter methylation was shown in many studies to be an important predictive biomarker for temozolomide (TMZ) resistance and poor progression-free survival in glioblastoma multiforme (GBM) patients. However, identifying the MGMT methylation status using molecular techniques remains challenging due to technical limitations, such as the inability to obtain tumor specimens, high prices for detection, and the high complexity of intralesional heterogeneity. To overcome these difficulties, we aimed to test the feasibility of using a novel radiomics-based machine learning (ML) model to preoperatively and noninvasively predict the MGMT methylation status. In this study, radiomics features extracted from multimodal images of GBM patients with annotated MGMT methylation status were downloaded from The Cancer Imaging Archive (TCIA) public database for retrospective analysis. The radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two-stage feature selection method, including an eXtreme Gradient Boosting (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. The cross-validation results suggested that the GA-based wrapper model achieved the high performance with a sensitivity of 0.894, specificity of 0.966, and accuracy of 0.925 for predicting the MGMT methylation status in GBM. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0.780, specificity 0.620, and accuracy 0.750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. The performance indicated that our model may potentially confer significant improvements in prognosis and treatment responses in GBM patients.
O6-甲基鸟嘌呤-DNA-甲基转移酶（MGMT）启动子甲基化在许多研究中被证明是替莫唑胺（TMZ）耐药和多形性胶质母细胞瘤（GBM）患者无进展生存期不良的重要预测生物标志物。然而，由于技术限制，如无法获得肿瘤标本、检测价格高以及肿瘤内异质性高的复杂性，使用分子技术确定 MGMT 甲基化状态仍然具有挑战性。为了克服这些困难，我们旨在测试使用新型基于放射组学的机器学习（ML）模型术前和非侵入性地预测 MGMT 甲基化状态的可行性。在这项研究中，从具有注释 MGMT 甲基化状态的 GBM 患者的多模态图像中提取的放射组学特征从癌症成像档案（TCIA）公共数据库中下载，用于回顾性分析。从磁共振成像（MRI）多模态图像中提取的放射组学特征经过两阶段特征选择方法，包括基于极端梯度提升（XGBoost）特征选择模型的特征选择，以及基于遗传算法（GA）的包装器模型，用于提取最有意义的预测性放射组学特征。交叉验证结果表明，基于 GA 的包装器模型的性能最高，其敏感性为 0.894，特异性为 0.966，准确性为 0.925，用于预测 GBM 中的 MGMT 甲基化状态。提取的 GBM 放射组学特征在低级别胶质瘤（LGG）数据集上的应用也实现了敏感性 0.780、特异性 0.620 和准确性 0.750，表明所选放射组学特征具有更广泛应用于低级别和高级别神经胶质瘤的潜力。该性能表明，我们的模型可能在 GBM 患者的预后和治疗反应方面带来显著改善。
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