论文标题
是否可以使用深度学习模型从脑肿瘤MRI扫描中预测MGMT启动子甲基化？
Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?
论文作者
论文摘要
胶质母细胞瘤是一种常见的大脑恶性肿瘤，往往在老年人中发生，几乎总是致命的。如果在称为MGMT启动子的肿瘤中的特定遗传序列是甲基化的，化学疗法的有效性（作为大多数癌症类型的标准治疗方法）可以改善。但是，为了确定MGMT启动子的状态，常规方法是进行活检以进行遗传分析，这是时间和精力消费。最近的几本出版物提出了MGMT启动子状态与肿瘤的MRI扫描之间的联系，因此建议将深度学习模型用于此目的。因此，在这项工作中，我们使用最广泛的数据集之一Brats 2021来研究采用深度学习解决方案（包括2D和3D CNN模型和视觉变压器）的效力。在对模型的性能进行了彻底的分析之后，我们得出结论，MRI扫描与MGMT启动子的状态之间似乎没有任何联系。
Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep learning models for this purpose. Therefore, in this work, we use one of the most extensive datasets, BraTS 2021, to study the potency of employing deep learning solutions, including 2D and 3D CNN models and vision transformers. After conducting a thorough analysis of the models' performance, we concluded that there seems to be no connection between the MRI scans and the state of the MGMT promoter.
