Langworthy Diversity Supplement: Image-based models of tumor-immune dynamics in glioblastoma

摘要 多形性胶质母细胞瘤(GBM)是最常见的胶质瘤，中位生存期14-18个月，尽管 积极的治疗方案。免疫疗法正在成为治疗癌症的一种很有前途的方法；然而， 我们无法确定早期反应，也无法预测谁将做出反应。这些不确定性构成了严重的 对能够有效应用免疫治疗方法的挑战。虽然活组织检查是最可靠的 为了评估肿瘤内的免疫情况，我们在空间和时间上都受到限制。 我们可以获得的活组织检查的数量，特别是对于脑瘤患者。肿瘤免疫的异质性 横跨患者的情况表明，需要针对患者的具体情况来准确评估每个患者 患者个体肿瘤免疫环境及其演变。作为家长助学金的一部分，我们将 使用非侵入性成像、图像引导活检、计算建模和人工智能来桥接 时空尺度和预测胶质瘤相关小胶质细胞/巨噬细胞(GAMs)的丰度 包括体素级别的每个磁共振图像(MRI)。将核磁共振与生物医学联系起来 使用放射组学方法的异质性提供了一个机会，使我们对 肿瘤免疫环境。具体地说，在这份补充材料中，我们将使用预测性肿瘤免疫图谱来 开发一种名为免疫获得天数(GDG)的免疫治疗反应指标，该指标基于现有的 增加的天数指标。GDG将被用来评估伽马种群的变化与治疗，如下所示 预见性地图。我们预计GDG将帮助了解谁将根据早期预测做出反应 地图发生了变化。此外，GDG指标将与其他免疫治疗反应的结果进行比较 指标，包括神经肿瘤学(伊朗)标准中的免疫治疗反应评估。

ABSTRACT Glioblastoma Multiforme (GBM) is the most common of all gliomas with a median survival 14-18 months, despite aggressive treatment regimens. Immunotherapy is emerging as a promising method to treat cancer; however, we are not able to identify early response or predict who will respond. These uncertainties pose serious challenges to being able to effectively apply immunotherapeutic approaches. While biopsies are the most reliable way to assess the immunological landscape within the tumor, we are limited both spatially and temporally in the number of biopsies we can obtain, particularly for brain tumor patients. The heterogeneity of the tumor-immune landscape across patients suggests that a patient-specific approach will be required to accurately assess each patient’s individual tumor-immune environment and the evolution thereof. As part of the Parent Grant, we will use non-invasive imaging, image-guided biopsies, computational modeling, and artificial intelligence to bridge spatial and temporal scales and predict the abundance of glioma associated microglia/macrophages (GAMMs) comprising each magnetic resonance image (MRI) at the voxel level. Linking the MRI to the biological heterogeneity using radiomics approaches provides an opportunity to individualize our understanding of the tumor-immune environment. Specifically, for this supplement, we will use the predictive tumor-immune maps to develop an immunotherapy response metric termed GAMMs Days Gained (GDG), which is based on the existing Days Gained metric. GDG will be used to evaluate the GAMM population changes with therapy as depicted by the predictive map. We expect that the GDG will aid in understanding who will respond based on early predictive map changes. Additionally, the GDG metric will be compared to results from other immunotherapy response metrics, including the standard immunotherapy response assessment in neuro-oncology (iRANO) criteria.