解决病理与基因数据不全时的生存预测难题，提升癌症预后准确性。
Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning
- 用超图学习融合多张切片与跨模态信息，缓解模态不平衡
- 在五个TCGA数据集上C指数提升超2.3%，不完整数据下表现更优
- 内存机制动态补全缺失数据，适合临床实际中不完整数据场景
多模态病理-基因分析对癌症生存预测至关重要。然而，现有方法主要整合福尔马林固定石蜡包埋（FFPE）切片与基因组数据，忽略了新鲜冷冻（FF）等其他保存方式的切片。同时，病理数据高分辨率空间特性易主导跨模态融合，导致病理与基因间模态失衡。此外，多数方法需完整模态数据，限制了在缺失病理或基因数据等临床常见情况下的应用。本文提出一种多模态生存预测框架，采用超图学习有效整合多全切片（WSI）信息及病理与基因间的跨模态交互，并缓解模态不平衡。引入记忆机制，存储先前学习的配对病理-基因特征，动态补偿不完整模态。在五个TCGA数据集上的实验表明，模型在C-Index上优于先进方法超过2.3%；在不完整模态情况下，显著超越仅病理（+3.3%）和仅基因（+7.9%）模型。代码已开源：https://github.com/MCPathology/M2Surv。
原文摘要 · Abstract (English)
Multimodal pathology-genomic analysis is critical for cancer survival prediction. However, existing approaches predominantly integrate formalin-fixed paraffin-embedded (FFPE) slides with genomic data, while neglecting the availability of other preservation slides, such as Fresh Froze (FF) slides. Moreover, as the high-resolution spatial nature of pathology data tends to dominate the cross-modality fusion process, it hinders effective multimodal fusion and leads to modality imbalance challenges between pathology and genomics. These methods also typically require complete data modalities, limiting their clinical applicability with incomplete modalities, such as missing either pathology or genomic data. In this paper, we propose a multimodal survival prediction framework that leverages hypergraph learning to effectively integrate multi-WSI information and cross-modality interactions between pathology slides and genomics data while addressing modality imbalance. In addition, we introduce a memory mechanism that stores previously learned paired pathology-genomic features and dynamically compensates for incomplete modalities. Experiments on five TCGA datasets demonstrate that our model outperforms advanced methods by over 2.3% in C-Index. Under incomplete modality scenarios, our approach surpasses pathology-only (3.3%) and gene-only models (7.9%). Code: https://github.com/MCPathology/M2Surv
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