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.

项目概要/摘要 该项目的目标是开发和评估使用多参数 MRI 的新型成像生物标志物 确定胶质脑肿瘤真实空间范围的方法。标准 RANO（反应评估） 神经肿瘤学）标准将肿瘤范围定义为造影剂后 T1w (T1+C) 上的亮信号区域 图像，称为对比增强病变 (CEL)，以及 T2w FLAIR 上的瘤周亮信号 图像，称为非增强病变（NEL）。然而，CEL 反映了血脑的通透性 对造影剂具有屏障作用，并且对于肿瘤和治疗效果可能表现出相同的效果。同样，尽管 NEL 可能含有肿瘤，目前的影像学无法区分肿瘤和水肿。这些困难导致 当前的解剖 MRI 方法无法确定神经胶质瘤的真实空间范围， 脑肿瘤患者的治疗管理受到严重限制。 我们和其他人已经证明先进的 MRI 方法，包括灌注和扩散 MRI，对于 评估肿瘤等级、预测结果或区分肿瘤与治疗效果。然而，几乎 排他地，该方法是从其中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。 尽管这种方法对于最初的生物标志物开发很有用，但它没有充分利用丰富的资源 多参数和空间信息可用，从而激发了当前的研究。一、两个多参数 将开发 MRI 生物标志物来识别增强和浸润的肿瘤负荷。那么，他们将是 单独和组合评估以评估与标准相比的总肿瘤负荷 当前使用的体积指标。 这些生物标志物的开发和测试将通过概述的几个独立步骤来完成 通过拟议的目标。首先（目标 1），我们建议开发一种 MRI 生物标志物，可以提供体素 增强 CEL 内肿瘤负荷的可能性，早期结果显示区分肿瘤的能力 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 范围内（目标 2）。这些努力利用了我们之前使用人工智能的成果、最近的进展 机器学习，以及我们独特的脑肿瘤组织库，其中包含数百个空间匹配的活检样本 到成像。最后（目标 3），将测试 CEL 和 NEL 内肿瘤负荷的空间范围 区分伪进展/反应与真实进展/反应，这是一个主要问题 混淆了当今的治疗管理。 总之，增强型和浸润性脑肿瘤的多参数先进 MRI 生物标志物具有 可能会导致治疗管理方式发生范式转变，最终改善结果。