Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. In glioblastoma, these biomarkers could potentially complement patient stratification strategies. We aim to develop and analytically validate radiological biomarkers that capture immune cell signatures within IDH-wildtype glioblastoma microenvironment using radiogenomic analysis. Methods: This was a retrospective multicenter study using curated open-access anonymized imaging and genomic data from TCGA-GBM, CPTAC, IvyGAP, REMBRANDT and CGGA datasets. Imaging data consisted of MRI-based radiomic features extracted from necrotic core, enhancing and edema regions of deep learning-based auto-segmented tumors. Radiomic feature selections were performed using nested cross-validated LASSO. Support vector machine and ensemble models were trained using seventeen immune and cell-specific score labels extracted from deconvoluted transcriptomic data using pan-cancer and glioblastoma immune signature matrices as reference standards. Seventeen classifier models trained in three cross-cohort strategies were validated on three held-out datasets assessing stability and generalizability. Results: One-hundred-and-seventy-six patients were included in the study. The immune-related radiomic signatures obtained after feature selection were shape, first order and higher order radiomic features. Models predicting macrophage subtype immune signature showed stable mean performance on balanced accuracy (0.67) and precision (0.89) metrics for three independent holdout datasets with ensemble model outperforming support vector machine model. Conclusion: Radiogenomic models non-invasively predicted the macrophage subtype M0 immune signature in IDH-wildtype glioblastoma. These biomarkers have the potential to stratify patients for immunotherapy within prospective glioblastoma clinical trials.

翻译：背景：放射基因组学能够识别基因组表型的放射学生物标志物。在胶质母细胞瘤中，这些生物标志物可能补充患者分层策略。我们旨在通过放射基因组学分析，开发并分析验证能够捕获IDH野生型胶质母细胞瘤微环境中免疫细胞特征的放射学生物标志物。方法：这是一项回顾性多中心研究，使用来自TCGA-GBM、CPTAC、IvyGAP、REMBRANDT和CGGA数据集的精选开放获取匿名影像和基因组数据。影像数据包括从深度学习自动分割肿瘤的坏死核心、强化区域和水肿区域提取的基于MRI的放射组学特征。使用嵌套交叉验证的最小绝对收缩和选择算子进行放射组学特征选择。以泛癌和胶质母细胞瘤免疫特征矩阵作为参考标准，从解卷积转录组数据中提取的17个免疫和细胞特异性评分标签，用于训练支持向量机和集成模型。在三种跨队列策略中训练的17个分类器模型在三个保留数据集上进行了验证，评估稳定性和泛化能力。结果：研究纳入176例患者。特征选择后获得的免疫相关放射组学特征包括形状、一阶和高阶放射组学特征。预测巨噬细胞亚型免疫特征的模型在三个独立保留数据集上的平均平衡准确率（0.67）和精确率（0.89）指标表现稳定，集成模型性能优于支持向量机模型。结论：放射基因组学模型无创预测了IDH野生型胶质母细胞瘤中的巨噬细胞亚型M0免疫特征。这些生物标志物有潜力在前瞻性胶质母细胞瘤临床试验中对患者进行免疫治疗分层。