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

项目摘要/摘要 本项目的目标是开发和评价使用多参数磁共振成像的新型成像生物标记物(S) 方法确定脑胶质瘤的真实空间范围。标准的RANO(响应评估 神经肿瘤学)标准将肿瘤范围定义为后造影剂T1w(T1 C)上的亮信号区 T2wFLAIR图像，称为对比度增强病变(CEL)，瘤周明亮信号 图像，称为非强化病变(NEL)。然而，CEL反映了血脑的通透性。 对造影剂有阻隔作用，对肿瘤和治疗效果看起来是一样的。同样，尽管内尔 可能含有肿瘤，目前的成像不能区分肿瘤和水肿病。这些困难导致了 目前的解剖MRI方法不能确定神经胶质瘤的真实空间范围 严重限制了脑肿瘤患者的治疗管理。 我们和其他人已经证明，先进的磁共振成像方法，包括灌注和扩散磁共振成像，对 评估肿瘤分级、预测结果或区分肿瘤和治疗效果。然而，几乎 唯一的方法是提取单个生理参数的平均值 预先确定感兴趣的肿瘤区域，然后测量它们与所需临床指标的相关性。 虽然这种方法对最初的生物标志物开发很有用，但它没有充分利用丰富的 多参数和空间信息可用，从而激励了当前的研究。第一，两个多参数 核磁共振生物标记物将被开发来识别增强和浸润性的肿瘤负担。那么，他们将会是 单独评估和联合评估总肿瘤负担与标准的比较 当前使用的体积度量。 这些生物标志物的开发和测试将分几个独立的步骤完成 由提议的目标决定。首先(目标1)，我们建议开发一种MRI生物标记物，它可以给出体素方向的 CEL内肿瘤负荷增加的可能性，早期结果显示有区分肿瘤的能力 从治疗效果来看。接下来，我们将开发一种能够识别浸润性肿瘤的多参数生物标志物 在NEL内(目标2)。这些努力利用了我们之前使用人工智能取得的成果，最近在 机器学习，以及我们独特的脑瘤组织库，其中包含数百个空间匹配的活检样本 为影像服务。最后(目标3)，将测试CEL和NEL内肿瘤负荷的空间范围 要区分伪进展/反应和真实进展/反应，这是一个主要问题， 混淆了今天的治疗管理。 综上所述，增强和浸润性脑肿瘤的多参数高级mri生物标志物具有 有可能导致治疗管理方式的范式转变，最终导致改善结果。

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.