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:随着医学诊断中深度学习应用的研究越来越多,这些系统通常被声称优于临床医生。然而,只有少数系统已经证明了医疗效果。从这个角度出发,我们研究了一系列用于评估胶质母细胞瘤的广泛深度学习算法,它是一种常见的老年人脑癌,并且具有致命性。手术、化疗和放疗是胶质母细胞瘤患者的标准治疗方法。MGMT启动子的甲基化状态,一种在肿瘤中发现的特定基因序列,影响了化疗的有效性。MGMT启动子的甲基化有助于提高化疗对多种癌症的响应和生存率。MGMT启动子的甲基化是通过肿瘤组织活检来确定的,然后进行基因检测。这个冗长而侵入性的过程增加了感染和其他并发症的风险。因此,研究人员使用深度学习模型来检查脑部MRI扫描的肿瘤,以确定MGMT启动子的甲基化状态。我们使用深度学习模型和一个包含585名参与者的最大公共MRI数据集来预测脑部MRI扫描中的胶质母细胞瘤肿瘤中MGMT启动子的甲基化状态。我们使用了Grad-CAM、遮挡敏感度、特征可视化和训练损失风景图对这些模型进行测试。我们的结果显示这两者之间不存在相关性,这表明应该使用外部队列数据来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。