The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medical efficacy. From this perspective, we examine a wide range of deep learning algorithms for the assessment of glioblastoma - a common brain tumor in older adults that is lethal. Surgery, chemotherapy, and radiation are the standard treatments for glioblastoma patients. The methylation status of the MGMT promoter, a specific genetic sequence found in the tumor, affects chemotherapy's effectiveness. MGMT promoter methylation improves chemotherapy response and survival in several cancers. MGMT promoter methylation is determined by a tumor tissue biopsy, which is then genetically tested. This lengthy and invasive procedure increases the risk of infection and other complications. Thus, researchers have used deep learning models to examine the tumor from brain MRI scans to determine the MGMT promoter's methylation state. We employ deep learning models and one of the largest public MRI datasets of 585 participants to predict the methylation status of the MGMT promoter in glioblastoma tumors using MRI scans. We test these models using Grad-CAM, occlusion sensitivity, feature visualizations, and training loss landscapes. Our results show no correlation between these two, indicating that external cohort data should be used to verify these models' performance to assure the accuracy and reliability of deep learning systems in cancer diagnosis.

翻译：关于医学诊断的深度学习研究数量正在增加，这些系统常被声称优于临床医生。然而，仅有少数系统展现出医学有效性。基于此视角，我们考察了用于评估胶质母细胞瘤（一种常见于老年人群且致命的脑部肿瘤）的多种深度学习算法。手术、化疗和放疗是胶质母细胞瘤患者的标准治疗方案。肿瘤中存在的特定基因序列——MGMT启动子的甲基化状态，会影响化疗的有效性。MGMT启动子甲基化可改善多种癌症的化疗反应和生存率。MGMT启动子甲基化状态通常通过肿瘤组织活检后进行基因检测来确定，这一冗长且侵入性的过程会增加感染及其他并发症的风险。因此，研究者采用深度学习模型通过脑部MRI扫描分析肿瘤以确定MGMT启动子的甲基化状态。我们使用深度学习模型及包含585名参与者的最大公共MRI数据集之一，通过MRI扫描预测胶质母细胞瘤肿瘤中MGMT启动子的甲基化状态。我们采用Grad-CAM、遮挡灵敏度、特征可视化及训练损失景观对这些模型进行测试。结果显示两者之间无相关性，表明应使用外部队列数据验证这些模型的性能，以确保深度学习系统在癌症诊断中的准确性和可靠性。