使用MRI扫描预测MGMT启动子甲基化状态？对深度学习模型的广泛实验评估
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models
AI总结：
本研究利用深度学习模型和包含585名参与者的公开MRI数据集预测胶质母细胞瘤的MGMT启动子甲基化状态，通过多种可视化方法测试发现模型性能与预期无相关性，强调需外部队列数据验证以确保诊断可靠性。
摘要：
用于医学诊断的深度学习研究数量正在增加，这些系统通常声称其表现优于临床医生。然而，只有少数系统证明了其医学疗效。从这个角度出发，我们考察了多种用于评估胶质母细胞瘤的深度学习算法——这是一种常见于老年人且致命的脑肿瘤。手术、化疗和放疗是胶质母细胞瘤患者的标准治疗方法。MGMT启动子是肿瘤中发现的一种特定基因序列，其甲基化状态会影响化疗的有效性。MGMT启动子甲基化能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过肿瘤组织活检来确定，随后进行基因检测。这种漫长且具有侵入性的操作会增加感染和其他并发症的风险。因此，研究人员利用深度学习模型通过脑部MRI扫描检查肿瘤，以确定MGMT启动子的甲基化状态。我们采用深度学习模型以及包含585名参与者的最大公开MRI数据集之一，通过MRI扫描预测胶质母细胞瘤中MGMT启动子的甲基化状态。我们使用Grad-CAM、遮挡敏感性、特征可视化和训练损失景观对这些模型进行测试。我们的结果显示这两者之间没有相关性，表明应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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
