Project 1: Predicting tumor biology from multiparametric MRI and image-guided tissue samples

项目 1：从多参数 MRI 和图像引导组织样本预测肿瘤生物学

基本信息

批准号：
10897350
负责人：
Janine Marie Lupo
金额：
$ 1.23万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
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/10897350
关键词：
Area Artificial Intelligence Biological Biological Assay Biological Monitoring Brain Neoplasms Categories Cellularity Characteristics Classification Clinical Data Data Analyses Data Set Decision Making Diffusion Magnetic Resonance Imaging Disease Excision Functional Imaging Glioma Gliosis Goals Heterogeneity Histologic Histopathology Hypoxia Image Infiltration Institution Intelligence Knowledge Learning Lesion Link Location Machine Learning Magnetic Resonance Imaging Malignant - descriptor Maps Metabolic Modality Modeling Molecular Multiparametric Analysis Mutation Newly Diagnosed Operative Surgical Procedures Patient Care Patients Perfusion Weighted MRI Proliferating Property Protocols documentation Radiation therapy Recurrence Recurrent tumor Residual Neoplasm Risk Sampling Scanning Source Standardization Statistical Models Subgroup Surrogate Markers Testing Time Tissue Sample Tumor Biology Work accurate diagnosis anatomic imaging clinical diagnosis cohort deep learning model direct patient care genomic biomarker image guided imaging biomarker imaging modality improved in vivo imaging innovation metabolic imaging molecular marker molecular subtypes multimodality multiparametric imaging non-invasive imaging novel novel strategies outcome prediction patient stratification prediction algorithm promoter prospective response spectroscopic imaging treatment response tumor tumor heterogeneity tumor progression

项目摘要

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). During previous cycles of our P01 and SPORE projects, 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 from tissue samples obtained from known image coordinates as well as advanced statistical-, machine-, and deep-learning models to construct spatial maps that predict tumor biology. In a new cohort of 400 patients with glioma (200 newly- diagnosed and 200 at the time of suspected recurrence) we will prospectively acquire multi-modal MRI and 1200 tissue samples with known image coordinates that are targeted based on our predictive spatial maps to both validate the best performing models in this independent test set and generate enhanced spatial maps to assess clinical value at time points that are critical for making decisions about patient care. In Aim 1, we will predict intra-tumoral heterogeneity and the extent of infiltrating tumor and in newly-diagnosed glioma using multi-parametric imaging from tissue samples with known imaging coordinates 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 innovative study will enhance and expand current strategies for evaluating patients with glioma and provide a framework for incorporating newly identified imaging, molecular, and genomic markers. This is imperative for intelligently combining novel imaging data and generating comprehensive predictive spatial maps that can be integrated with current response assessment criteria for evaluating standard and experimental treatments.

项目摘要这项工作的目的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，用于临床的组织样品诊断来自相对较小的异质病变的一部分，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异质性和串行监测肿瘤的生物学特性对于评估对治疗的反应至关重要并指导患者护理。在量化恶性特征的替代标记方面表现出最有希望的方式在神经胶质瘤的患者中，包括扩散加权MRI，灌注加权MRI和1H MR光谱学成像（MRSI）。在我们的P01和孢子项目的先前周期中，我们积累了多参数来自外科扫描的生理和代谢成像数据，以便针对2000多个组织样品。超过750例神经胶质瘤患者。这些样本是独一无二的靶向肿瘤生物学的异质区域，包括：缺氧，增殖，细胞性，神经胶质病和使用解剖，生理和代谢成像的结合使用恶性转化。使用这个很好特征是队列，我们的新方法将利用组织样品的多参数成像特征从已知的图像坐标以及高级统计，机器和深度学习模型获得构建预测肿瘤生物学的空间图。在新的400例神经胶质瘤患者的队列中（200例新近诊断为疑似复发时被诊断为200 具有已知图像坐标的组织样品，这些图像坐标是根据我们的预测空间图靶向的验证此独立测试集中最佳性能模型，并生成增强的空间图以评估时间点的临床价值对于做出有关患者护理的决策至关重要。在AIM 1中，我们将预测肿瘤内异质性以及浸润肿瘤的程度以及新诊断的使用来自已知成像坐标的组织样品的多参数成像，以识别恶性特征的领域将指导组织采样以进行更准确的诊断并预测残留疾病的空间位置和特征。 AIM 2将定义与治疗相关的特征改变胶质瘤的低级分子亚组中的复发性肿瘤和恶性转化在接受手术的患者中，可疑肿瘤进展。这项创新的研究将增强和扩展当前的策略，以评估神经胶质瘤患者并提供一个结合新鉴定的成像，分子和基因组标记的框架。这是必须的智能地结合了新颖的成像数据并生成可以是的全面预测空间图与当前的响应评估标准集成了评估标准和实验治疗的标准。