Multimodal deep learning has improved prognostic accuracy for brain tumours by integrating histopathology and genomic data, yet the contribution of volumetric MRI within unified survival frameworks remains unexplored. This pilot study extends a bimodal framework by incorporating Fluid Attenuated Inversion Recovery (FLAIR) MRI from BraTS2021 as a third modality. Using the TCGA-GBMLGG cohort (664 patients), we evaluate three unimodal models, nine bimodal configurations, and three trimodal configurations across early, late, and joint fusion strategies. In this small cohort setting, trimodal early fusion achieves an exploratory Composite Score (CS = 0.854), with a controlled $Δ$CS of +0.011 over the bimodal baseline on identical patients, though this difference is not statistically significant (p = 0.250, permutation test). MRI achieves reasonable unimodal discrimination (CS = 0.755) but does not substantially improve bimodal pairs, while providing measurable uplift in the three-way combination. All MRI containing experiments are constrained to 19 test patients, yielding wide bootstrap confidence intervals (e.g. [0.400,1.000]) that preclude definitive conclusions. These findings provide preliminary evidence that a third imaging modality may add prognostic value even with limited sample sizes, and that additional modalities require sufficient multimodal context to contribute effectively.

翻译：多模态深度学习通过整合组织病理学与基因组数据提升了脑肿瘤预后预测的准确性，但三维容积MRI在统一生存分析框架中的贡献尚未被探索。本项初步研究通过引入BraTS2021数据集中的液体衰减反转恢复序列（FLAIR）MRI作为第三种模态，扩展了原双模态框架。基于TCGA-GBMLGG队列（664例患者），我们评估了三种单模态模型、九种双模态配置及三种三模态配置，涵盖早期融合、晚期融合与联合融合策略。在此小样本队列条件下，三模态早期融合获得探索性综合评分（CS=0.854），相较于相同患者的双模态基线具有受控的ΔCS=+0.011提升，但该差异未达统计学显著性（p=0.250，置换检验）。MRI单模态达到合理判别能力（CS=0.755），但未显著改善双模态组合性能，而在三模态组合中呈现可测量的增益。所有含MRI的实验均限于19例测试患者，导致宽泛的bootstrap置信区间（如[0.400,1.000]），无法得出确定性结论。这些发现初步表明，即使在有限样本量下，第三种成像模态仍可能带来预后增益，且额外模态需具备充分的多模态语境方能有效发挥作用。