Statistical atlases of brain tumor MRI:do imaging phenotypes predict progression?

脑肿瘤 MRI 统计图谱：成像表型能否预测进展？

• 批准号: 7369127
• 负责人: Christos Davatzikos
• 金额: $ 34.45万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7369127
• 关键词: Aggressive course; Aging; Anatomic Models; Anatomy; Area; Atlases; Biomechanics; Biopsy; Brain; Brain Diseases; Brain Neoplasms; Brain imaging; Cancer Patient; Cessation of life; Characteristics; Classification; Clinical; Clinical Research; Communities; Computer Assisted; Conformal Radiotherapy; Data; Databases; Development; Diagnostic; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Disease Progression; Dose; Edema; Excision; Facility Construction Funding Category; Fiber; Foundations; Frequencies; Genetic; Glioma; Goals; Gray unit of radiation dose; Health; Histopathology; Image; Image Analysis; Infiltration; Investigation; Lead; Libraries; Link; Literature; Location; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of brain; Measurement; Methodology; Methods; Modality; Modeling; Nature; Operative Surgical Procedures; Outcome; Pathologic; Pathway interactions; Patients; Pattern; Pattern Recognition; Perfusion; Phase; Phenotype; Pilot Projects; Population; Positioning Attribute; Predictive Factor; Predictive Value; Preventive; Process; Proliferating; Protocols documentation; Radiation; Radiation therapy; Recurrence; Relative (related person); Reproducibility; Research; Scanning; Signal Transduction; Spectrum Analysis; Standards of Weights and Measures; Structure; Time; Tissues; Work; base; brain tissue; cancer cell; cancer recurrence; image registration; improved; mathematical model; neuroimaging; outcome forecast; simulation; soft tissue; statistics; tool; treatment planning; tumor; tumor growth; tumor progression; white matter

DESCRIPTION (provided by applicant): Statistical atlases, and associated image analysis methods, have found widespread use in several neuroimaging fields, presenting a powerful way to integrate diverse imaging information, correlate it with genetic and clinical measurements, understand effects of disease on brain structure and function, and construct diagnostic tools. This proposal will combine statistical image analysis, deformable registration, and biophysical modeling approaches to an integrated framework for constructing and clinically using statistical atlases from brain tumor patients. Emphasis is placed on gliomas, which have very poor prognosis due to cancer infiltration beyond the visible tumor boundary. Accordingly, the ultimate clinical goal of this study is to identify subtle imaging characteristics of brain tissue that is likely to be infiltrated by tumor, as well as of tissue that is likely to present recurrence in relatively shorter time period. This will be achieved by studying the multi-modal imaging phenotypes of healthy and pathologic tissues in conjunction with spatial information, including the spatial pattern of the tumor and the proximity of malignant tissue to white matter fiber pathways, and by correlating these phenotypes with clinical information, including tumor recurrence. The hypothesis is that signal and spatial information together will be able to identify brain tissues that are likely to later present recurrence. The main technical challenges that will be overcome are 1) development of computationally efficient biophysical models of tumor growth, diffusion, and mass effect; 2) development of deformable registration methods that will allow us to co-register tumor-bearing brain images and build a population-based atlas-the main challenges here are to estimate the appropriate tumor parameters as well as the location of peri-tumor anatomy that is typically confounded by edema, infiltration and extreme deformations; and 3) development of machine learning methods for characterizing subtle abnormalities of brain tissue, and for identifying tissue that is likely to present recurrence after resection and treatment. Pilot studies on the feasibility of this approach to larger clinical studies will be performed on a database of brain MR images obtained from glioma patients via a rich and extensive acquisition protocol, including perfusion, diffusion tensor imaging, spectroscopy, and conventional imaging.

描述（由申请人提供）：统计地图集和相关的图像分析方法已广泛用于几个神经成像领域，提供了一种强大的方法来整合各种成像信息，将其与遗传和临床测量相关联，了解疾病对大脑结构和功能的影响，并构建诊断工具。该提案将结合联合收割机统计图像分析，可变形注册，和生物物理建模方法的综合框架，用于构建和临床使用脑肿瘤患者的统计图谱。重点放在神经胶质瘤上，由于癌症浸润超过可见的肿瘤边界，其预后非常差。因此，本研究的最终临床目标是识别可能被肿瘤浸润的脑组织以及可能在相对较短的时间内出现复发的组织的细微成像特征。这将通过结合空间信息研究健康和病理组织的多模态成像表型来实现，包括肿瘤的空间模式和恶性组织与白色纤维通路的接近度，并通过将这些表型与临床信息（包括肿瘤复发）相关联来实现。假设是信号和空间信息一起将能够识别可能稍后出现复发的脑组织。将克服的主要技术挑战是：1）发展计算效率高的肿瘤生长、扩散和质量效应的生物物理模型; 2）开发可变形配准方法，使我们能够共同配准肿瘤脑图像并建立基于人群的图谱-这里的主要挑战是估计适当的肿瘤参数以及肿瘤位置-肿瘤解剖学通常被水肿、浸润和极端变形混淆;以及3）开发机器学习方法，用于表征脑组织的细微异常，并用于识别在切除和治疗后可能出现复发的组织。将在通过丰富而广泛的采集协议（包括灌注、扩散张量成像、光谱学和常规成像）从胶质瘤患者获得的脑部MR图像数据库上对这种方法在更大规模临床研究中的可行性进行试点研究。