Image-based models of tumor-immune dynamics in glioblastoma

胶质母细胞瘤肿瘤免疫动力学的基于图像的模型

基本信息

批准号：
10361416
负责人：
Peter Canoll
金额：
$ 81.49万
依托单位：
MAYO CLINIC ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-01 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10361416
关键词：
Advanced Malignant Neoplasm Anti-Inflammatory Agents Area Artificial Intelligence Behavior Therapy Biological Biophysics Biopsy Cancerous Cells Classification Clinical Complex Computer Models Data Decision Making Diagnostic Edema Environment Evolution Future Glioblastoma Glioma Goals Heterogeneity Image Image Enhancement Immune Immune system Immunologics Immunology Immunotherapy In Situ Inflammation Inflammatory Link Location Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of brain Maps Measures Microglia Modeling Molecular Oncology Operative Surgical Procedures Outcome Pathway interactions Patient-Focused Outcomes Patients Phenotype Physics Population Predisposition Prognosis Recurrence Resources Role Scientist Sex Differences Signal Transduction Spatial Distribution Specimen System Testing Therapeutic Therapeutic Intervention Time Tumor-infiltrating immune cells Variant anticancer research base biological heterogeneity cancer care cell type clinical decision-making clinical imaging cohort cytotoxic effective therapy in vivo insight machine learning model macrophage molecular phenotype neoplastic cell novel strategies outcome prediction personalized approach predictive modeling quantitative imaging radiomics response serial imaging sex spatiotemporal standard of care synergism tool tumor tumor behavior tumor growth tumor microenvironment tumor progression tumor-immune system interactions

项目摘要

ABSTRACT The use of immunotherapy to treat cancer continues to generate hope and excitement among those involved in cancer care and research. However, our inability to explain why some patients do not respond to immunotherapy, combined with our inability to identify early response or predict the responders, poses serious challenges in this field. Currently, biopsies serve as the most informative way to assess the immunological activity within a cancerous area, but we are spatially and temporally limited in the number of biopsies we can obtain from patients, especially in cases of brain cancer. Clear evidence of tumor-immune environment heterogeneity across patients suggests that we will have to use an individualized approach in order to accurately assess patient tumor’s specific immune environment and the evolution of these complex systems. We propose to use computational modeling and artificial intelligence to bridge the spatial scales of the cellular content comprising each MRI at the voxel level, but also to bridge the temporal scales. We will focus on the most cellular immune population in glioblastoma, microglia/macrophages, that constitute as much as 50% of the cellular content of tumor specimens. By fusing MRI with the biological heterogeneity found in image- localized biopsies through such radiomics approaches provides an opportunity to individualize our understanding of the the tumor-immune environment, broadly benefiting scientists across the fields of oncology and immunology. In addition to providing a deeper understanding of the tumor at every imaging time point, the radiomics maps can also be used to parameterize dynamic mechanistic models of tumor growth to allow for prediction of future dynamics. These spatio-temporal models allow us to test hypotheses about causal relationships between different cell types and microenvironmental factors, as well as to verify whether the radiomics maps provide early dynamic insights into tumor response that can impact clinical decision making.

抽象的使用免疫疗法治疗癌症继续给相关人员带来希望和兴奋然而，我们无法解释为什么有些患者对癌症治疗没有反应。免疫疗法，加上我们无法识别早期反应或预测反应者，造成了严重的后果目前，活检是评估免疫学信息最丰富的方法。癌区域内的活动，但我们可以进行的活检数量在空间和时间上都受到限制从患者身上获得的，尤其是脑癌病例中的肿瘤免疫环境的明确证据。患者之间的异质性表明，我们必须使用个体化的方法来准确评估患者肿瘤的特定免疫环境和这些复杂系统的进化。我们建议使用计算建模和人工智能来弥合细胞的空间尺度内容包括体素水平上的每个 MRI，但也为了弥合时间尺度，我们将重点关注胶质母细胞瘤中大多数细胞免疫群体是小胶质细胞/巨噬细胞，占胶质母细胞瘤细胞免疫群体的 50% 通过将 MRI 与图像中发现的生物异质性相融合通过这种放射组学方法进行局部活检提供了个体化我们的机会对肿瘤免疫环境的了解，使肿瘤学领域的科学家广泛受益和免疫学除了在每个成像时间点提供对肿瘤的更深入的了解之外，放射组学图也可用于参数化肿瘤生长的动态机制模型，以允许这些时空模型使我们能够检验有关因果关系的假设。不同细胞类型和微环境因素之间的关系，以及验证是否放射组学图提供了对肿瘤反应的早期动态洞察，可以影响临床决策。