New treatment monitoring biomarkers for brain tumors using multiparametric MRI with machine learning

使用多参数 MRI 和机器学习监测脑肿瘤生物标志物的新治疗方法

• 批准号: 10220248
• 负责人: KATHLEEN Marie SCHMAINDA
• 金额: $ 54.77万
• 依托单位: MEDICAL COLLEGE OF WISCONSIN
• 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/10220248
• 关键词: Address; Anatomy; Area; Artificial Intelligence; Biological Markers; Biopsy Specimen; Brain Neoplasms; Brain region; Clinical; Contrast Media; Data; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Edema; Enhancing Lesion; Excision; Glioblastoma; Glioma; Goals; Image; Imaging Techniques; Individual; Lesion; MGMT gene; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methods; Modeling; Molecular; Monitor; Newly Diagnosed; Patients; Perfusion; Physiological; Prediction of Response to Therapy; Primary Lesion; Probability; Recurrence; Signal Transduction; Testing; Tissue Banks; Tissue Sample; Training; Tumor Burden; Tumor Markers; Work; bevacizumab; biomarker development; blood-brain barrier permeabilization; chemoradiation; contrast enhanced; convolutional neural network; imaging biomarker; improved outcome; indexing; interest; magnetic resonance imaging biomarker; molecular marker; neuro-oncology; novel; novel marker; outcome prediction; predictive modeling; prospective; response; treatment effect; tumor

Project Summary/Abstract The goal of this project is to develop and evaluate novel imaging biomarker(s) that use multiparameter MRI methods to identify the true spatial extent of glial brain tumors. The standard RANO (response assessment in neuro-oncology) criteria define tumor extent as the region of bright signal on post-contrast agent T1w (T1+C) images, termed the contrast enhancing lesion (CEL), along with the peritumoral bright signal on T2w FLAIR images, referred to as non-enhancing lesion (NEL). Yet, the CEL reflects the permeability of the blood-brain barrier to contrast agent and can appear the same for both tumor and treatment effect. Likewise, though NEL likely contains tumor, current imaging cannot distinguish tumor from edema. These difficulties result in the inability of current anatomical MRI methods to determine the true spatial extent of glial tumors, a serious limitation for treatment management of brain tumor patients. We and others have shown that advanced MRI methods, including perfusion and diffusion MRI, are useful for assessing tumor grade, predicting outcomes, or distinguishing tumor from treatment effect. Yet, almost exclusively, the approach has been to extract mean values of a single physiological parameter from predetermined tumor regions of interest and then measure their correlation with the desired clinical index. Although this approach has been useful for initial biomarker development, it underutilizes the rich multiparameter and spatial information available, thus motivating the current study. First, two multiparameter MRI biomarkers will be developed to identify enhancing and infiltrating tumor burden. Then, they will be evaluated individually and in combination to assess the total tumor burden in comparison with the standard volumetric metrics in current use. The development and testing of these biomarkers will be accomplished in several independent steps outlined by the proposed aims. First (Aim 1), we propose to develop an MRI biomarker that gives the voxelwise probability of enhancing tumor burden within CEL, with early results showing the ability to distinguish tumor from treatment effect. Next, we will develop a multiparameter biomarker capable of identifying infiltrating tumor within NEL (Aim 2). These efforts leverage our previous results using artificial intelligence, recent advances in machine learning, and our unique brain tumor tissue bank with hundreds of biopsy samples spatially matched to imaging. Finally (Aim 3), the spatial extent of tumor burden within CEL and NEL will be tested in their ability to distinguish pseudo-progression/response from true progression/response, which is a primary question that confounds treatment management today. In summary, multiparameter advanced MRI biomarkers of enhancing and infiltrative brain tumor have the potential to cause a paradigm shift in how treatment is managed, ultimately resulting in improved outcomes.

项目摘要/摘要 该项目的目的是开发和评估使用多参数MRI的新型成像生物标志物 确定神经胶质脑肿瘤的真实空间范围的方法。标准RANO（响应评估 神经肿瘤学标准将肿瘤范围定义为对比后剂T1W（T1+C）上明亮信号的区域 图像，称为对比度增强病变（CEL），以及T2W Flair上的周围明亮信号 图像，称为非增强病变（NEL）。然而，CEL反映了血脑的渗透性 造影剂的障碍物，对于肿瘤和治疗效果都可以看起来相同。同样，虽然尼尔 可能包含肿瘤，当前的成像无法将肿瘤与水肿区分开。这些困难导致 当前的解剖学MRI方法无法确定神经胶质肿瘤的真实空间范围，A 严重限制脑肿瘤患者的治疗管理。 我们和其他人表明，包括灌注和扩散MRI在内的高级MRI方法对 评估肿瘤等级，预测结局或区分肿瘤与治疗效果。但是，几乎 完全是，该方法是从中提取单个生理参数的平均值 预定的感兴趣的肿瘤区域，然后测量其与所需的临床指数的相关性。 尽管这种方法对于初始生物标志物的开发很有用，但它不足以富人 可用的多参数和空间信息，从而激发了当前的研究。首先，两个多参数 将开发MRI生物标志物来确定增强和渗透肿瘤负担。然后，他们会 与标准相比，分别评估并组合评估总肿瘤负担 当前使用中的体积指标。 这些生物标志物的开发和测试将在概述的几个独立步骤中完成 由拟议的目的。首先（AIM 1），我们建议开发一种MRI生物标志物，以提供VoxelWise 增强CEL内肿瘤负担的概率，早期结果表明能够区分肿瘤 从治疗效果。接下来，我们将开发一个能够识别浸润肿瘤的多参数生物标志物 在内尔（AIM 2）内。这些努力利用人工智能利用我们先前的结果，最近的进步 机器学习，以及我们独特的脑肿瘤组织库，具有数百个活检样品的空间匹配 进行成像。最后（AIM 3），CEL和NEL内肿瘤负担的空间范围将以其能力进行测试 为了区分伪产生/响应与真实进步/响应，这是一个主要问题 今天混淆了治疗管理。 总而言之，多参数的高级MRI生物标志物的增强和浸润性脑肿瘤具有 可能导致治疗方式的范式转变的潜力，最终导致结果改善。