Advanced preoperative assessment of meningiomas

脑膜瘤的高级术前评估

基本信息

批准号：
10322718
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
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10322718
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

项目摘要

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.

项目摘要由蛛网膜细胞产生的脑膜瘤构成所有颅内肿瘤的> 1/3。虽然通常是良性的，但这些肿瘤会导致质量作用和周围水肿引起的临床症状。在需要的情况下干预，总切除术，尽可能提供最佳结果。但是，治疗策略是最终通过确定手术难度与患者整体之间的适当平衡来决定健康。两个机械性能是手术难度的重要预测指标：肿瘤僵硬和粘附到周围的组织。在手术前了解这些特性将使临床医生更准确评估手术风险并确定最有效的治疗策略。机械性能很难预测通过常规成像方法，但可以通过磁共振弹性直接评估（MRE）和相关滑动界面成像（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僵硬显着相关。此外，SII准确地预测了脑膜瘤和前庭schwannomas中的肿瘤粘附。仍然，仍然存在挑战以使这些发现在临床上影响。为了估计刚度，主要限制在于分辨率。因此，在AIM 1中，我们将开发一个体素 - 明智的肿瘤刚度分类器。这个目标将基于我们最近发表的基于神经网络的反转（nni），它表现出比模拟中常规直接反演的表现优越脑。在AIM 1A中，我们将通过实施更复杂的神经网络体系结构和为培训创建更现实的模拟。在AIM 1B中，进步将在幻影中验证不均匀的刚度。最后，在AIM 1C借助我们的神经外科合作者，我们将收集一个大量的手术僵硬评估样本。我们将使用这些评估来训练Voxel的刚度分类器，然后将在单独的测试集中进行验证。这个目标将导致一张传达两者的地图对预测的僵硬和信心对外科医生的临床意义。 SII中最适合的局限性包括对图像的主观解释和空间缺乏解决的预测。 AIM 2将通过开发Voxel Slip接口分类器来应对这些挑战。在 AIM 2A，我们将研究基于神经网络的滑动接口预测指标，以添加到我们当前的方法中。在 AIM 2B，我们将评估这种新方法是否可以改善幻影实验的预测。在AIM 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Voxel分类器。这此目的的结果将是肿瘤依从性的地图，表示易于解释的概率。综上所述，这些目标将为神经外科医生提供临床上重要的信息以改善患者管理。更广泛地说，该项目中的技术进步将影响整个MRE领域。