Diversity Supplement: 5P01CA118816 Project 1

项目摘要 这项工作的目标是评估肿瘤异质性的体素预测空间图的临床价值 直接反映组织病理学定义的肿瘤生物学。众所周知，用于临床的组织样品 诊断来自一个巨大异质性病变的相对较小的部分，且很少获得 在疾病的过程中。能够评估肿瘤内的非侵入性成像标记物 异质性和连续监测肿瘤生物学特性对于评估对治疗的反应是至关重要的 指导病人护理 在量化恶性特征的替代标志物方面显示出最有希望的模式 包括弥散加权MRI、灌注加权MRI和1H MR波谱 磁共振成像（MRSI）。我们已经积累了多参数生理和代谢成像数据， 手术扫描，以针对来自750多名神经胶质瘤患者的2000多个组织样本。这些 样品是独特的，因为它们每个都是专门选择的，以靶向肿瘤的异质区域 生物学，包括：缺氧，增殖，细胞结构，神经胶质增生和恶性转化，使用组合 解剖、生理和代谢成像。使用这种特征良好的队列，我们的新方法将 利用多参数成像功能，结合先进的统计、机器和深度成像技术， 学习模型来预测肿瘤生物学、分子表型和进展。 目的1侧重于预测肿瘤内异质性和浸润性肿瘤的程度，并在新的- 诊断胶质瘤，以确定恶性特征的区域，这将直接组织取样， 准确诊断和预测残留病灶的空间位置和特征。目标2将定义 治疗相关变化与复发肿瘤和低级别恶性转化的特征 因疑似肿瘤进展而接受手术的患者中胶质瘤的分子亚组。 该补充将允许开发和纳入新的机器学习方法， 现有的数据，以及学习的成像功能，是预测新定义的分子亚组， 比世界卫生组织先前定义的2016年标准更具预后性的胶质瘤 organization.这一结果将加强和扩大目前评估神经胶质瘤患者的策略， 提供了一个框架，用于整合新鉴定的成像、分子和基因组标记， 与当前的反应评估标准相结合，用于评估标准和实验性治疗。

PROJECT SUMMARY The goal of this work is to assess the clinical value of voxel-wise predictive spatial maps of tumor heterogeneity that directly reflect histopathologically defined tumor biology. It is well known that tissue samples used for clinical diagnosis come from a relatively small portion of a vastly heterogenous lesion and are obtained infrequently during the course of the disease. Non-invasive imaging markers that are able to assess intratumoral heterogeneity and serially monitor biological properties of the tumor are critical for assessing response to therapy and directing patient care. The modalities that have shown the most promise in quantifying surrogate markers of malignant characteristics in patients with gliomas include diffusion-weighted MRI, perfusion-weighted MRI, and 1H MR spectroscopic imaging (MRSI). We have accumulated multi-parametric physiologic and metabolic imaging data from pre- surgical scans in order to target over 2000 tissue samples from more than 750 patients with glioma. These samples are unique in that they have each been specifically selected to target heterogeneous regions of tumor biology, including: hypoxia, proliferation, cellularity, gliosis, and malignant transformation using a combination of anatomic, physiologic, and metabolic imaging. Using this well-characterized cohort, our novel approach will leverage multi-parametric imaging features in conjunction with advanced statistical-, machine-, and deep- learning models to predict tumor biology, molecular phenotype, and progression. Aim 1 focuses on predicting intra-tumoral heterogeneity and the extent of infiltrating tumor and in newly- diagnosed glioma in order to identify areas of malignant characteristics that will direct tissue sampling for a more accurate diagnosis and predict the spatial location and characteristics of residual disease. Aim 2 will define characteristics of treatment related changes vs recurrent tumor and malignant transformation within lower grade molecular sub-groups of glioma within patients undergoing surgery for suspected tumor progression. This supplement will allow for the development and incorporation of new machine learning approaches on our existing data as well as learn the imaging features that are predictive of newly-defined molecular subgroups of glioma that are more prognostic of outcome than previously defined 2016 criteria by the World Health Organization. The result will enhance and expand current strategies for evaluating patients with glioma and provide a framework for incorporating newly identified imaging, molecular, and genomic markers that can be integrated with current response assessment criteria for evaluating standard and experimental treatments.