Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA.
Department of Neurology, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA.
J Digit Imaging. 2017 Oct;30(5):622-628. doi: 10.1007/s10278-017-0009-z.
Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p < 0.001). We report a method that alleviates the need of extensive preprocessing and acts as a proof of concept that deep neural architectures can be used to predict molecular biomarkers from routine medical images.
利用 MRI 成像预测 O6-甲基鸟嘌呤甲基转移酶（MGMT）基因甲基化状态非常重要，因为它是脑肿瘤反应和预后的预测因子。在这项研究中，我们比较了三种不同的剩余深度神经网络（ResNet）架构，以评估它们在无需进行特定肿瘤分割步骤的情况下预测 MGMT 甲基化状态的能力。我们发现 ResNet50（50 层）架构是表现最佳的模型，在测试集中的准确率为 94.90%（+/- 3.92%）（对切片进行无肿瘤、甲基化 MGMT 或非甲基化 MGMT 的分类）。ResNet34（34 层）的准确率为 80.72%（+/- 13.61%），而 ResNet18（18 层）的准确率为 76.75%（+/- 20.67%）。ResNet50 的性能明显优于 ResNet18 和 ResNet34 两种架构（p<0.001）。我们报告了一种方法，减轻了广泛预处理的需求，并证明了深度神经网络架构可用于从常规医学图像中预测分子生物标志物。
