Advanced preoperative assessment of meningiomas

脑膜瘤的高级术前评估

• 批准号: 9897634
• 负责人: Matthew Christopher Murphy
• 金额: $ 35.78万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9897634
• 关键词: Acoustic Neuroma; Address; Adherence; Algorithms; Arachnoid mater; Benign; Brain; Cells; Clinic; Clinical; Collaborations; Complex; Counseling; Data; Detection; Development; Disease; Edema; Environment; Equilibrium; Excision; Goals; Health; Image; Image Analysis; Imaging Techniques; Incidence; Intervention; Intracranial Neoplasms; Knowledge; Lesion; Light; Location; Machine Learning; Magnetic Resonance Elastography; Magnetic Resonance Imaging; Maps; Measures; Methods; Network-based; Neurosurgeon; Noise; Operative Surgical Procedures; Outcome; Patients; Performance; Pituitary Gland Adenoma; Probability; Procedures; Property; Publishing; Research; Resolution; Risk; Sampling; Skull Base Neoplasms; Surgeon; Symptoms; Testing; Time; Tissues; Training; Translating; United States; Work; artificial neural network; base; clinical practice; effective therapy; experience; experimental study; heuristics; human subject; imaging approach; imaging modality; improved; magnetic field; mechanical properties; meningioma; multimodality; neural network; neural network architecture; neurosurgery; predictive modeling; simulation; surgical risk; tool; treatment choice; treatment strategy; tumor

PROJECT SUMMARY Meningiomas, which arise from arachnoid cells, make up >1/3 of all intracranial tumors. While typically benign, these tumors induce clinical symptoms due to mass effect and peritumoral edema. In cases requiring intervention, gross total resection provides the best outcomes when possible. However, treatment strategy is ultimately decided by determining the proper balance between surgical difficulty and the patient's overall health. Two mechanical properties are important predictors of surgical difficulty: tumor stiffness and adherence to surrounding tissues. Knowledge of these properties before surgery would allow clinicians to more accurately assess surgical risk and identify the most effective treatment strategy. Mechanical properties are difficult to predict by conventional imaging approaches, but can be directly assessed by Magnetic Resonance Elastography (MRE) and related Slip Interface Imaging (SII). In published studies, we have shown that MRE-based stiffness estimates are significantly correlated with tumor stiffness in meningiomas and pituitary adenomas. Furthermore, SII accurately predicted tumor adherence in meningiomas and vestibular schwannomas. Still, challenges remain to make these findings clinically-impactful. For estimating stiffness, the primary limitation lies in resolution. Therefore, in Aim 1 we will develop a voxel- wise classifier of tumor stiffness. This aim will build on our recently published neural network-based inversion (NNI), which has demonstrated superior performance to conventional direct inversions in simulation and in the brain. In Aim 1a, we will advance NNI by implementing more complex neural network architectures and creating more realistic simulations for training. In Aim 1b, the advances will be validated in a phantom with inhomogeneous stiffness. Finally, in Aim 1c with the aid of our Neurosurgery collaborators, we will collect a large sample of surgical stiffness assessments. We will use these assessments to train a voxel-wise stiffness classifier, which will then be validated in a separate test set. This aim will result in a map that conveys both stiffness and confidence in the prediction on a scale that is clinically meaningful to surgeons. The most-pressing limitations in SII include the subjective interpretation of the images and the lack of spatially resolved predictions. Aim 2 will address these challenges by developing a voxel-wise slip interface classifier. In Aim 2a, we will investigate a neural network-based predictor of slip interfaces to add to our current methods. In Aim 2b, we will evaluate if this new method can improve predictions in phantom experiments. In Aim 2c, we will again leverage surgical assessments of meningioma adherence to train and test a voxel-wise classifier. The result of this aim will be a map of tumor adherence represented as an easily interpreted probability. Taken together, these aims will provide neurosurgeons with clinically-important information to improve patient management. More broadly, technical advances made in this project will impact the entire MRE field.

项目概要 脑膜瘤由蛛网膜细胞产生，占所有颅内肿瘤的 1/3 以上。虽然通常是良性的， 这些肿瘤由于占位效应和瘤周水肿而诱发临床症状。在需要的情况下 干预、大体全切除术可在可能的情况下提供最佳结果。但治疗策略是 最终通过确定手术难度和患者整体状况之间的适当平衡来决定 健康。两种机械特性是手术难度的重要预测因素：肿瘤硬度和粘附性 至周围组织。手术前了解这些特性将使临床医生能够更准确地 评估手术风险并确定最有效的治疗策略。机械性能难以预测 通过传统成像方法，但可以通过磁共振弹性成像直接评估 (MRE) 和相关的滑动界面成像 (SII)。在已发表的研究中，我们表明基于 MRE 的刚度 估计值与脑膜瘤和垂体腺瘤的肿瘤硬度显着相关。 此外，SII 准确预测了脑膜瘤和前庭神经鞘瘤中的肿瘤粘附。仍然， 要使这些发现具有临床影响力仍然存在挑战。 对于估计刚度，主要限制在于分辨率。因此，在目标 1 中，我们将开发一个体素 肿瘤硬度的明智分类器。这一目标将建立在我们最近发表的基于神经网络的反演基础上 （NNI），它在模拟和反演中表现出了优于传统直接反演的性能 脑。在目标 1a 中，我们将通过实现更复杂的神经网络架构来推进 NNI 创建更真实的训练模拟。在目标 1b 中，进展将在模型中得到验证 刚度不均匀。最后，在目标 1c 中，在我们的神经外科合作者的帮助下，我们将收集 手术硬度评估的大样本。我们将使用这些评估来训练体素方面的刚度 分类器，然后将在单独的测试集中进行验证。这个目标将产生一张传达以下两个信息的地图： 预测的刚度和置信度对外科医生来说具有临床意义。 SII 最紧迫的局限性包括图像的主观解释和缺乏空间信息 解决了预测。目标 2 将通过开发体素滑动界面分类器来解决这些挑战。在 目标 2a，我们将研究基于神经网络的滑移界面预测器，以添加到我们当前的方法中。在 目标 2b，我们将评估这种新方法是否可以改进模型实验中的预测。在目标 2c 中，我们将 再次利用脑膜瘤依从性的手术评估来训练和测试体素分类器。这 这一目标的结果将是肿瘤粘附图，以易于解释的概率表示。 总而言之，这些目标将为神经外科医生提供临床重要信息，以改善患者的病情 管理。更广泛地说，该项目取得的技术进步将影响整个地雷危害教育领域。