Virtual Biopsy with Tissue-level Accuracy in Glioma

神经胶质瘤中具有组织水平精度的虚拟活检

• 批准号: 10596130
• 负责人: Joseph A Maldjian
• 金额: $ 61.85万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10596130
• 关键词: 19q; Acceleration; Algorithms; Applications Grants; Artificial Intelligence; Automation; Biology; Biomedical Engineering; Biopsy; Brain; Brain Neoplasms; Classification; Clinical; Computerized Medical Record; Craniotomy; Data; Data Set; Databases; Digital Imaging and Communications in Medicine; Excision; Glioma; Goals; Human; Hyperacusis; Image; Institution; Knowledge; MGMT gene; Magnetic Resonance Imaging; Manuals; Medical center; Methods; Methylation; Molecular; Molecular Analysis; Morphologic artifacts; Motion; Neurosurgical Procedures; Noise; Operative Surgical Procedures; Patient Care; Patient-Focused Outcomes; Patients; Performance; Predictive Value; Procedures; Process; Prognosis; Prospective cohort; Reporting; Research Project Grants; Resources; Risk; Sample Size; Specificity; T2 weighted imaging; Testing; The Cancer Genome Atlas; The Cancer Imaging Archive; Time; Tissue Sample; Tissues; Training; Tumor Tissue; United States National Institutes of Health; Validation; Work; clinical decision-making; clinical implementation; clinical translation; contrast imaging; cost; deep learning; deep learning algorithm; experience; improved; large datasets; learning classifier; learning strategy; molecular marker; motion sensitivity; mutational status; novel; prospective; response; surgical risk; tool; tumor; virtual biopsy

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.

项目摘要 这是一项生物工程研究补助金（BRG）提案，以响应Par-19-158，以进一步发展和 使用标准验证最关键的神经胶质瘤分子标记（IDH，1p/19q，MGMT）的非侵入性面板 临床MRI T2加权图像和深度学习，并将性能扩展到组织级的精度。 当前，获得分子标记状态的唯一可靠方法是通过直接组织采样 肿瘤需要颅骨切开术和立体定向活检或大型开放手术切除。无创 确定具有组织水平准确性的分子标记物将在管理方面是转化的 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 是时候进行确定治疗，改善患者经验以及最终的患者结局和生存时间。 人工智能（例如深度学习）已成为一种分类成像数据的有力方法 这可以超越人类的表现。使用我们的新型体素分类细分的初步工作 NIH/NCI TCIA Glioma数据库的方法优于任何先前的无创方法 确定IDH，1P/19Q和MGMT甲基化，达到97％，93％和95％的精度， 分别。然而，该方法需要在TCIA之外进行验证，并且准确性需要是 扩展以实现组织水平的性能。这将通过使用我们的表现最佳来实现 Voxel分类框架，利用特定于标记的目标样本量，并获得最终提升 来自深度学习的伪影校正网络。 在AIM 1中，我们将策划一个超过2000个神经瘤的数据库，其中包括我们机构的500名受试者，1200名受试者 来自我们的外部合作者，以及TCIA的300多名主题。我们将训练我们的素素的深度学习 基于临床T2加权MR图像确定分子状态的分类器 97％。在AIM 2中，我们将严格评估网络的运动和噪声敏感性并创建人工制品 校正网络的目标是1）在大量运动/噪声和 2）即使没有可见的伪像，也进一步提高了组织水平的性能。在目标3中，我们将 部署完整的端到端临床工作流程，并评估300上AI工具的现实世界实时性能 前瞻性地从我们的外部合作者那里获得了脑肿瘤病例和300名受试者。 AI工具将是 可在其他医疗中心部署。开发的框架也可以扩展到 以简单的方式进行其他标记。总而言之，该BRG提案将进一步发展，完善和 验证一种基于非侵入性MRI的方法来确定最关键的胶质瘤分子标记 组织水平的精度显着降低，在许多情况下，消除了立体定向活检的需求。