Imaging signatures of genetic mutations in glioblastoma using machine learning

摘要胶质母细胞瘤(Gb)是最常见、最具侵袭性的成人恶性脑肿瘤。 预后和异质性的分子和影像资料。尽管目前适用的 在过去的20年中，治疗选择(即手术、放射治疗、化疗)有所扩大 几年来，操作系统费率没有实质性的改善。治疗基底膜的主要障碍 患者是他们的分子景观的异质性。分子靶标的测定 需要体外手术后组织分析，这在评估肿瘤的空间方面是有限的 异质性(单一样本组织病理学和分子分析造成的抽样误差)和 时间上的异质性(不可能连续地评估分子的转变 治疗期间的肿瘤)。在这里，我们建议发展定量成像现象(Qip)。 GB中一系列感兴趣的突变的标记。我们将在以前关于EGFR、IDH1的工作的基础上再接再厉 突变和MGMT甲基化QIP特征，并开发广泛的成像小组 10个基因突变的特征，以及MGMT启动子甲基化，使用机器学习 方法应用于临床相对常规的mpMRI(标准加扩散张量和灌注 协议)。这些生物标志物的可获得性有助于非侵入性I)患者分层 转化为适当的处理，ii)个体分子特征的测量。特别是， 我们建议实现以下具体目标： 具体目标1(SA1)：制定建立量化指标的有利方法 GB突变的表型特征成像 特定目标2(SA2)：建立胶质瘤中10个感兴趣突变的QIP特征，加上 MGMT启动子状态，使用下一代测序(NGS)。我们将使用709个数据集。 具体目标3(SA3)：使用QIP签名表征GB的分子异质性， 利用SA1的NGS样本，以及我们将对其进行分型的新样本，添加到 共从150例患者的4个不同部位采集了600份肿瘤标本。这个 作为正在进行的工作的一部分，最初的150个组织样本已经进行了分析。 特定目标4(SA4)：将我们的方法集成到癌症成像表型组学工具包中 (CaPTk)，以便用户能够轻松访问它们

Glioblastoma (GB) is the most common and aggressive malignant adult brain tumor, with grim prognosis and heterogeneous molecular and imaging profiles. Although the currently applicable treatment options (i.e., surgery, radiotherapy, chemotherapy) have expanded during the last 20 years, there is no substantial improvement in the OS rates. The major obstacle in treating GBM patients is the heterogeneity of their molecular landscape. Determination of molecular targets requires ex vivo postoperative tissue analyses, which are limited in assessing the tumor's spatial heterogeneity (sampling error due to single sample histopathological and molecular analysis) and temporal heterogeneity (not possible to continuously assess the molecular transformation of the tumor during treatment). Herein we propose to develop quantitative imaging phenomic (QIP) markers of a range of mutations of interest in GB. We will build on prior work on EGFR, IDH1 mutations and MGMT methylation QIP signatures, and develop an extensive panel of imaging signatures of 10 gene mutations, as well as MGMT promoter methylation, using machine learning methods applied to relatively routine clinical mpMRI (standard plus diffusion tensor and perfusion protocols). Availability of such biomarkers can contribute to non-invasive i) patient stratification into appropriate treatments, ii) measurement of individual molecular characteristics. In particular, we propose to carry out the following specific aims: Specific Aim 1 (SA1): To develop the enabling methodologies for constructing Quantitative Imaging Phenomic signatures of GB mutations Specific Aim 2 (SA2): Establish QIP signatures of 10 mutations of interest in gliomas, plus MGMT promoter status, using next generation sequencing (NGS). We will use 709 datasets. Specific Aim 3 (SA3): Characterize the molecular heterogeneity of GB using QIP signatures, leveraging the NGS samples of SA1, as well as a new sample that we will genotype, adding to a total of 600 tumor samples obtained from 4 different locations per patient from 150 patients. The first 150 tissue samples are already analyzed as part of ongoing work. Specific Aim 4 (SA4): Integrate our methods into the Cancer Imaging Phenomics Toolkit (CaPTk), in order to allow easy access to them by users