Accurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

中文翻译：

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深度学习卷积神经网络ResNet101和放射组学特征准确分析mpMRI成像，通过迁移学习方法预测MGMT启动子甲基化状态

准确的脑肿瘤分类对于提高胶质母细胞瘤患者的诊断、预后和治疗至关重要。我们采用 ResNet101 深度学习方法和迁移学习来分析 2021 年北美放射学会 (RSNA) 脑肿瘤挑战数据集。该数据集包含四个结构磁共振成像 (MRI) 序列：流体衰减反转恢复 (FLAIR)、T1 加权预对比 (T1w)、T1 加权后对比 (T1Gd) 和 T2 加权 (T2) 。我们使用标准评估指标评估了模型的性能。检测胶质母细胞瘤患者 MGMT 甲基化状态的最高性能是准确度 (85.48%)、敏感性 (80.64%)、特异性 (90.32%)。而没有肿瘤的分类性能则具有准确度（85.48%）、敏感性（90.32%）、特异性（80.64%）。使用集成袋装树和浮雕特征选择方法 (30/74) 计算的放射组学特征 (74) 将验证精度提高了 84.3%，检测 AUC 提高了 0.9038。氧6胶质母细胞瘤患者的甲基鸟嘌呤 DNA 甲基转移酶 (MGMT) 启动子甲基化状态有望优化治疗计划和预后。通过了解 MGMT 甲基化状态，临床医生可以就治疗策略做出明智的决定，从而可能改善临床结果。