Virtual Biopsy with Tissue-level Accuracy in Glioma

项目摘要 这是一个生物工程研究资助（BRG）的建议，以响应PAR-19-158，以进一步发展和 使用标准方法验证一组最关键的神经胶质瘤分子标志物（IDH，1 p/19 q，MGMT）的非侵入性 临床MRI T2加权图像和深度学习，并将性能扩展到组织级精度。 目前，获得分子标记状态的唯一可靠方法是通过直接组织取样， 肿瘤，需要开颅术和立体定向活检或大的开放手术切除。无创 具有组织水平准确性的分子标记物的确定将在管理 神经胶质瘤，减少或消除与神经外科手术相关的风险和成本，加速 确定治疗的时间，改善患者体验，最终改善患者结局和生存时间。 深度学习等人工智能已经成为成像数据分类的强大方法 可以超越人类的表现。初步工作使用我们的新的体素明智的分类分割 NIH/NCI TCIA胶质瘤数据库的方法优于任何先前的非侵入性方法， IDH、1 p/19 q和MGMT甲基化的测定，准确率分别为97%、93%和95%， 分别然而，该方法需要在TCIA之外进行验证，并且需要 延长以实现组织级性能。这将通过使用我们的顶级性能 体素分类框架，利用特定于标记的目标样本大小，并获得最终提升 深度学习伪影校正网络。 在目标1中，我们将整理一个包含2000多个胶质瘤的数据库，其中包括来自我们机构的500名受试者，1200名受试者， 来自我们的外部合作者，以及来自TCIA的300多名受试者。我们将训练我们的体素深度学习 基于临床T2加权MR图像确定分子状态的分类器，目标精度为 百分之九十七在目标2中，我们将严格评估网络的运动和噪声敏感度，并创建伪影 校正网络的目标是：1）在大量运动/噪声的设置中恢复精度， 2)甚至在没有可见伪影的情况下也进一步提高组织级性能的准确性。在目标3中， 部署完整的端到端临床工作流程，并在300台机器上评估AI工具的真实实时性能 前瞻性获得的脑肿瘤病例和300例外部合作者的受试者。AI工具将是 可在其他医疗中心部署。开发的框架还可以扩展到 以简单的方式添加标记。总之，BRG提案将进一步发展、完善和 验证一种非侵入性的基于MRI的方法，用于确定最关键的神经胶质瘤分子标记物， 组织水平的准确性，以显着减少，并在许多情况下消除立体定向活检的需要。

Project Summary This is a Bioengineering Research Grant (BRG) proposal in response to PAR-19-158 to further develop and validate a non-invasive panel of the most critical glioma molecular markers (IDH, 1p/19q, MGMT) using standard clinical MRI T2-weighted images and deep learning, and extend the performance to tissue-level accuracies. Currently, the only reliable way of obtaining molecular marker status is through direct tissue sampling of the tumor, requiring either a craniotomy and stereotactic biopsy or a large open surgical resection. Noninvasive determination of molecular markers with tissue-level accuracy would be transformational in the management of gliomas, reducing or eliminating the risks and costs associated with a neurosurgical procedure, accelerating the time to definitive treatment, improving patient experience and ultimately patient outcomes and survival time. Artificial intelligence such as deep learning has emerged as a powerful method for classification of imaging data that can exceed human performance. Preliminary work using our novel voxel-wise classification-segmentation approach with the NIH/NCI TCIA glioma database has outperformed any prior noninvasive methods for determination of IDH, 1p/19q, and MGMT methylation, achieving accuracies of 97%, 93%, and 95%, respectively. The approach however, needs to be validated beyond the TCIA and accuracies need to be extended in order to achieve tissue level performance. This will be accomplished by using our top-performing voxel-wise classification framework, leveraging marker-specific targeted sample sizes, and gaining a final boost from deep-learning artifact correction networks. In Aim 1 we will curate a database of over 2000 gliomas including 500 subjects from our institution, 1200 subjects from our external collaborators, and over 300 subjects from the TCIA. We will train our voxel-wise deep learning classifiers to determine molecular status based on clinical T2-weighted MR images with target accuracies of 97%. In Aim 2 we will rigorously evaluate the motion and noise sensitivity of the networks and create an artifact correction network with the goals of 1) recovering accuracies in the setting of large amounts of motion/noise and 2) further boosting accuracy to tissue-level performance even in the absence of visible artifact. In Aim 3 we will deploy a complete end-to-end clinical workflow and evaluate real-world live performance of the AI tool on 300 prospectively acquired brain tumor cases and 300 subjects from our external collaborators. The AI tool will be made available for deployment at other medical centers. The developed framework can also be extended to additional markers in a straightforward fashion. In summary, this BRG proposal will further develop, refine and validate a non-invasive MRI-based method for determining the most critical glioma molecular markers rivaling tissue-level accuracies to significantly reduce and in many cases eliminate the need for stereotactic biopsy.