Methylation of the O⁶-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of MGMT status in 87 glioblastoma patients with contrast-enhanced T1W images and 66 with fluid-attenuated inversion recovery(FLAIR) images. The end-to-end pipeline completes both tumor segmentation and status classification. The better tumor segmentation performance comes from FLAIR images (Dice score, ) compared to contrast-enhanced T1WI (Dice score, ), and the better status prediction is also from the FLAIR images (accuracy, ; recall, ; precision, ; and score, ). This proposed pipeline not only saves the time in tumor annotation and avoids interrater variability in glioma segmentation but also achieves good prediction of MGMT methylation status. It would help find molecular biomarkers from routine medical images and further facilitate treatment planning.

中文翻译：

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基于深度学习的MR图像分析自动预测胶质母细胞瘤中MGMT的状态

O ^6-甲基鸟嘌呤甲基转移酶（MGMT）基因启动子的甲基化与胶质母细胞瘤患者当前护理标准的有效性相关。在这项研究中，设计了一条深度学习管道，用于自动预测87例胶质母细胞瘤患者的MGMT状态，这些患者具有对比增强的T1W图像和66例具有液体衰减倒置恢复（FLAIR）图像。端到端流水线同时完成了肿瘤分割和状态分类。更好的肿瘤分割性能来自FLAIR图片（骰子得分，）与对比增强型T1WI（骰子得分，），更好的状态预测也来自FLAIR图片（准确性，; 召回，; 精确，; 和得分）。拟议中的管道不仅节省了肿瘤注释的时间，避免了脑胶质瘤分割中的变异性，而且还实现了对MGMT甲基化状态的良好预测。这将有助于从常规医学图像中找到分子生物标志物，并进一步促进治疗计划。