Predicting brain tumor progression via multiparametric image analysis and modelin

通过多参数图像分析和建模预测脑肿瘤进展

基本信息

批准号：
8915752
负责人：
Christos Davatzikos
金额：
$ 54.12万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-06-01 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8915752
关键词：
Accounting Anatomy Atlases Biological Preservation Biophysics Biopsy Brain Brain Neoplasms Cell Density Cells Cessation of life Characteristics Clinical Clinical Treatment Communities Complex Computer Analysis Computer Simulation Data Data Set Databases Disease Edema Equilibrium Excision Failure Fiber Future Genetic Genotype Glioblastoma Glioma Goals Growth Health Heterogeneity Image Image Analysis Infiltration Label Learning Literature Machine Learning Malignant - descriptor Malignant Neoplasms Metabolism Methods Modeling Molecular Molecular Analysis Molecular Profiling Normal tissue morphology Pathway interactions Patients Pattern Pattern Recognition Phenotype Physiology Process Property Protocols documentation Radiation therapy Radiosurgery Reading Recurrence Scanning Signal Transduction Solutions Source Technology Tissues Training Vascularization Work biophysical model brain tissue cancer imaging chemotherapy clinical practice cohort design follow-up image registration imaging informatics improved innovation insight mathematical model neoplastic cell neuroimaging neuropsychiatry outcome forecast patient population population based predictive modeling radiosensitizing social statistics targeted treatment trend tumor tumor growth tumor progression white matter

项目摘要

DESCRIPTION (provided by applicant): High-grade brain gliomas, the most common of which is glioblastoma multiforme (GBM), have terrible prognosis and a median patient survival of about 12 months. Although combinations of surgical removal, radiotherapy and chemotherapy are used in the clinical practice, a fundamental and persistent limitation in treating these aggressive tumors is that they tend to infiltrate into normal tissue well beyond margins visible via imaging. Since assessing the spatial extent of tumor infiltration is nearly impossible using current radiologic reading practices, clinical treatment tends to be restricted to parts that are deemed to be clearly malignant, frequently only the enhancing tumor. This failure to aggressively treat the infiltrating tumor accelerates tumor recurrence, and eventually patient death. This proposal aims to develop computational modeling and image analysis methods that will improve our ability to estimate GBM infiltration, as well as to predict tissue that is likelyto present fastest tumor recurrence, thereby eventually opening the way for more aggressive, yet targeted, treatment, such as targeted aggressive surgical removal and/or radiosurgery. To achieve our goal, we will integrate information from several sources: 1) advanced multi-parametric imaging, which captures many aspects of tumor anatomy and physiology~ 2) computational modeling of tumor growth and infiltration~ 3) machine learning methods which, after appropriate training, can learn subtle and potentially complex imaging phenotypes of infiltrating tumors~ 4) statistical atlases, which capture population-based trends that can offer additional insights into tumor growth, such as relationship of infiltration to vasculature and to white matter fiber pathways~ 5) data from one of the largest patient populations having advanced imaging, genotyping, follow-up till tumor recurrence, and histological analysis.

描述（由申请人提供）：高级脑胶质瘤，其中最常见的是多形胶质母细胞瘤（GBM），具有可怕的预后，中位患者的存活率约为12个月。尽管在临床实践中使用了手术切除，放疗和化学疗法的组合，但治疗这些攻击性肿瘤的基本和持久限制是，它们倾向于通过成像可见的缘远远超出距离的正常组织。由于使用当前的放射性阅读实践评估肿瘤浸润的空间范围几乎是不可能的，因此临床治疗往往仅限于被认为显然是恶性的部分，通常只有增强的肿瘤。这种未能积极治疗浸润性肿瘤会加速肿瘤复发，并最终患者死亡。该提案旨在开发计算建模和图像分析方法，以提高我们估计GBM浸润的能力，并预测可能呈现最快肿瘤复发的组织，从而最终为更具侵略性但有针对性的治疗（例如有针对性的攻击性手术移除和/或放射性手术）开辟道路。 To achieve our goal, we will integrate information from several sources: 1) advanced multi-parametric imaging, which captures many aspects of tumor anatomy and physiology~ 2) computational modeling of tumor growth and infiltration~ 3) machine learning methods which, after appropriate training, can learn subtle and potentially complex imaging phenotypes of infiltrating tumors~ 4) statistical atlases, which capture population-based trends that can offer additional insights into tumor生长，例如浸润与脉管系统的关系和与白质纤维途径的关系〜5）来自具有高级成像，基因分型，随访直至肿瘤复发的最大患者人群之一的数据和组织学分析。