Image-based models of tumor-immune dynamics in glioblastoma

摘要 免疫疗法治疗癌症的使用继续给那些参与其中的人带来希望和兴奋。 癌症护理和研究。然而，我们无法解释为什么一些患者对 免疫疗法，再加上我们无法识别早期反应或预测应答者，造成了严重的后果 这一领域的挑战。目前，活组织检查是评估免疫学上最有价值的方法。 癌症区域内的活动，但我们可以进行的活组织检查在空间和时间上都是有限的 从患者那里获得，特别是在脑癌的情况下。肿瘤免疫环境的明确证据 患者的异质性表明，我们将不得不使用个性化的方法，以便 准确评估患者肿瘤的特定免疫环境和这些复杂系统的演变。 我们建议使用计算建模和人工智能来连接细胞的空间尺度 包括体素水平上的每个MRI的内容，而且还桥接时间尺度。我们将重点关注 胶质母细胞瘤中的大多数细胞免疫群体，小胶质细胞/巨噬细胞，构成多达50% 肿瘤标本的细胞含量。通过将MRI与图像中发现的生物异质性融合在一起- 通过这种放射组学方法进行的局部活检提供了一个机会，使我们的 对肿瘤免疫环境的了解，使肿瘤学领域的科学家普遍受益 和免疫学。除了在每个成像时间点提供对肿瘤的更深层次的了解外， 放射组学地图还可以用于对肿瘤生长的动态机制模型进行参数化，以允许 对未来动态的预测。这些时空模型允许我们测试关于因果关系的假设 不同细胞类型与微环境因素之间的关系，以及验证 放射组学地图提供了对肿瘤反应的早期动态洞察，这可能会影响临床决策。

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.