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

• 批准号: 10524208
• 负责人: Peter Canoll
• 金额: $ 7.4万
• 依托单位: MAYO CLINIC ARIZONA
• 依托单位国家: 美国
• 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/10524208
• 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

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.

摘要