Glioma grading and survival prediction require the integration of heterogeneous information collected at different spatial and biological scales. Histopathology describes tissue morphology, mRNA expression captures molecular activity, and magnetic resonance imaging provides a non-invasive view of tumor extent and radiological heterogeneity. Existing glioma prognosis models often combine only two of these sources, while their alignment objectives remain mostly pairwise. This paper introduces GLORIA, a novel trimodal framework for GLioma Omics - Radiology - hIstopathology Alignment. GLORIA processes whole-slide image regions, gene-expression profiles, and 3D MRI volumes through modality-specific encoders, projects them into a shared latent space, and aligns them with a Gramian contrastive loss that measures the volume spanned by the three modality embeddings. The aligned representations are fused through a cross-modal gating module and optimized jointly for three-class glioma grading and overall survival prediction. We evaluate GLORIA on a matched TCGA-GBM/LGG and BraTS21 cohort, comprising 132 patients with all three modalities. On the shared trimodal test set, GLORIA improves over the bimodal WSI-mRNA baseline in all the metrics considered.

翻译：神经胶质瘤分级与生存预测需要整合来自不同空间和生物学尺度的异质性信息。组织病理学描述组织形态，mRNA表达捕捉分子活性，磁共振成像提供肿瘤范围及放射学异质性的非侵入性观察。现有神经胶质瘤预后模型通常仅融合其中两种数据源，且对齐目标多停留在成对层面。本文提出GLORIA——一种用于神经胶质瘤组学、影像学与组织病理学三方对齐的新型三模态框架。GLORIA通过模态专用编码器处理全切片图像区域、基因表达谱与三维MRI体素，将其投影至共享潜在空间，并采用Gram矩阵对比损失（该损失衡量三种模态嵌入所张成的体量）实现对齐。对齐后的表征经跨模态门控模块融合，联合优化用于三级神经胶质瘤分级与总体生存预测。我们在包含132例匹配TCGA-GBM/LGG与BraTS21三模态数据患者的队列上评估GLORIA。在三模态共享测试集中，GLORIA在所有评估指标上均优于双模态WSI-mRNA基线模型。