Diversity Supplement: 5P01CA118816 Project 1

项目总结 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。 这直接反映了组织病理学定义的肿瘤生物学。众所周知，用于临床的组织样本 诊断来自极不均匀的病变的相对较小的部分，并且很少获得 在疾病的过程中。能够评估肿瘤内的非侵入性成像标志物 肿瘤的异质性和连续监测生物学特性是评估治疗反应的关键。 并指导病人护理。 在量化恶性特征的替代标记物方面最有希望的方法 在胶质瘤患者中，包括弥散加权MRI、灌注加权MRI和1H磁共振波谱 成像(MRSI)。我们已经积累了多参数的生理和代谢成像数据 为了针对750多名胶质瘤患者的2000多个组织样本进行外科扫描。这些 样本的独特之处在于，每个样本都是专门针对肿瘤的不同区域进行选择的 生物学，包括：缺氧、增殖、细胞性、胶质细胞增生症和恶性转化 解剖、生理和代谢成像。使用这一特征良好的队列，我们的新方法将 将多参数成像功能与高级统计、机器和深度成像功能相结合 预测肿瘤生物学、分子表型和进展的学习模型。 目的1着重于预测肿瘤内的异质性和浸润性肿瘤的程度，以及在新的 为了确定胶质瘤的诊断区域恶性特征，将直接对组织进行更多的采样 准确诊断和预测残留病的空间位置和特征。目标2将定义 低级别肿瘤复发和恶变的治疗相关变化特征 疑似肿瘤进展而接受手术的患者中胶质瘤的分子亚群。 本补充资料将允许开发和整合新的机器学习方法 现有数据以及了解预测新定义的分子亚群的成像特征 与世界卫生组织之前定义的2016年标准相比，胶质瘤对预后的影响更大 组织。这一结果将加强和扩大目前评估胶质瘤患者和 提供一个框架，用于整合新识别的成像、分子和基因组标记，这些标记可以 与现行的反应评估标准相结合，以评估标准和试验性治疗。

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.