通过影像组学预测胶质母细胞瘤中巨噬细胞免疫特征，助力免疫治疗分层。
Predictive Radiomics for Evaluation of Cancer Immune SignaturE in Glioblastoma: the PRECISE-GBM study
- 基于多中心影像与基因组数据，提取肿瘤区域的影像特征进行筛选。
- 集成模型在三个独立数据集上稳定预测巨噬细胞亚型，准确率达0.67。
- 为临床试验中免疫治疗患者分层提供无创生物标志物，适合神经肿瘤研究者。
背景：影像基因组学可识别影像生物标志物以表征基因表型。在胶质母细胞瘤中，这些标志物可能补充患者分层策略。本研究旨在利用影像基因组分析，开发并验证用于捕捉IDH野生型胶质母细胞瘤微环境中免疫细胞特征的影像生物标志物。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数据集的经整理的公开匿名影像与基因组数据。影像数据包括深度学习自动分割肿瘤后，从坏死核心、增强区和水肿区提取的MRI影像组学特征。采用嵌套交叉验证LASSO进行影像组学特征选择。基于泛癌及胶质母细胞瘤免疫特征矩阵，从去卷积转录组数据中提取17个免疫与细胞特异性评分标签，训练支持向量机与集成模型。在三种交叉队列策略下训练的17个分类器模型，在三个保留数据集上进行验证，评估其稳定性与泛化能力。结果：共纳入176例患者。特征选择后获得的免疫相关影像组学标志物包括形态、一阶及高阶影像特征。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表现稳定，平均平衡准确率为0.67，精确度为0.89；集成模型优于支持向量机模型。结论：影像基因组模型可非侵入性预测IDH野生型胶质母细胞瘤中的巨噬细胞亚型M0免疫特征。这些生物标志物有望在前瞻性胶质母细胞瘤临床试验中用于免疫治疗患者的分层。
原文摘要 · Abstract (English)
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.
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