Diversity Supplement: 5P01CA118816 Project 1

多样性补充：5P01CA118816 项目 1

• 批准号: 10381390
• 负责人: Susan M Chang
• 金额: $ 8.77万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10381390
• 关键词: Adult; Area; Artificial Intelligence; Biological; Biological Assay; Biological Monitoring; Cellularity; Characteristics; Clinical; Data; Data Analyses; Data Set; Development; Diffusion Magnetic Resonance Imaging; Disease; Excision; Functional Imaging; Glioma; Gliosis; Goals; Heterogeneity; Histologic; Histopathology; Hypoxia; Image; Intelligence; Knowledge; Learning; Lesion; Link; Location; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Maps; Modality; Modeling; Molecular; Multiparametric Analysis; Newly Diagnosed; Operative Surgical Procedures; Outcome; Patients; Perfusion Weighted MRI; Population Statistics; Primary Brain Neoplasms; Property; Protocols documentation; Radiation therapy; Recurrence; Recurrent tumor; Residual Tumors; Risk; Sampling; Scanning; Source; Standardization; Statistical Models; Subgroup; Surrogate Markers; Survival Rate; Therapeutic Trials; Tissue Sample; Tissue imaging; Tumor Biology; Work; World Health Organization; accurate diagnosis; anatomic imaging; clinical Diagnosis; cohort; deep learning; direct patient care; genomic biomarker; image guided; imaging biomarker; imaging modality; improved; in vivo imaging; innovation; metabolic imaging; molecular marker; molecular phenotype; multimodality; non-invasive imaging; novel; novel strategies; outcome prediction; parent grant; prognostic; response; spectroscopic imaging; statistical learning; tumor; tumor heterogeneity; tumor progression

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.

项目概要 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，组织样本用于临床 诊断来自于异质性病变的相对较小部分，并且很少获得 在疾病过程中。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的生物学特性对于评估治疗反应至关重要 并指导患者护理。 在量化恶性特征替代标志物方面最有希望的方法 神经胶质瘤患者的检查包括弥散加权 MRI、灌注加权 MRI 和 1H MR 波谱 成像（MRSI）。我们积累了前期的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 样本的独特之处在于它们都是针对肿瘤的异质区域专门选择的 生物学，包括：缺氧、增殖、细胞结构、神经胶质增生和恶性转化 解剖学、生理学和代谢成像。利用这个特征鲜明的群体，我们的新颖方法将 利用多参数成像功能与先进的统计、机器和深度分析相结合 预测肿瘤生物学、分子表型和进展的学习模型。 目标 1 侧重于预测肿瘤内异质性和浸润肿瘤的程度以及新的 诊断出神经胶质瘤，以确定恶性特征的区域，从而指导组织取样以获得更多信息 准确诊断并预测残留病灶的空间位置和特征。目标 2 将定义 治疗相关变化与低级别肿瘤复发和恶变的特征 因疑似肿瘤进展而接受手术的患者中神经胶质瘤的分子亚组。 该补充将允许在我们的系统上开发和合并新的机器学习方法 现有数据以及学习可预测新定义的分子亚组的成像特征 与世界卫生组织先前定义的 2016 年标准相比，神经胶质瘤的预后更好 组织。该结果将增强和扩展当前评估神经胶质瘤患者的策略 提供了一个框架，用于整合新发现的成像、分子和基因组标记，这些标记可以 与当前的反应评估标准相结合，用于评估标准和实验治疗。