融合影像、病理与基因数据，提升胶质瘤生存预测准确率。
Trimodal Deep Learning for Glioma Survival Prediction: A Feasibility Study Integrating Histopathology, Gene Expression, and MRI
- 首次将FLAIR MRI纳入三模态深度学习框架，探索其对生存预测的贡献。
- 三模态早期融合达到0.854的综合评分，较双模态提升0.011但不显著。
- 小样本下影像模态仍有潜力，但需足够多模态上下文才能有效增益。
多模态深度学习通过整合病理与基因数据提升了脑肿瘤预后预测准确性，但体积化MRI在统一生存预测框架中的作用尚不清楚。本初步研究将原有双模态框架扩展为包含来自BraTS2021的FLAIR MRI作为第三模态。基于TCGA-GBMLGG队列（664例患者），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，采用早期、晚期和联合融合策略。在小样本条件下，三模态早期融合取得探索性综合评分（CS=0.854），相对于相同患者上的双模态基线提升ΔCS=+0.011，但未达统计显著（p=0.250，置换检验）。MRI单独预测表现合理（CS=0.755），但对双模态组合提升有限；而在三模态联合中产生可测量的增益。所有含MRI的实验均受限于19名测试患者，导致置信区间极宽（如[0.400,1.000]），无法得出确定结论。这些发现提供了初步证据：即使在小样本下，引入第三种影像模态仍可能带来预后价值，但新增模态需具备足够的多模态上下文才能有效贡献。
原文摘要 · Abstract (English)
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.
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