The integration of histology images and gene profiles has shown great promise for improving survival prediction in cancer. However, current approaches often struggle to model intra- and inter-modal interactions efficiently and effectively due to the high dimensionality and complexity of the inputs. A major challenge is capturing critical prognostic events that, though few, underlie the complexity of the observed inputs and largely determine patient outcomes. These events, manifested as high-level structural signals such as spatial histologic patterns or pathway co-activations, are typically sparse, patient-specific, and unannotated, making them inherently difficult to uncover. To address this, we propose SlotSPE, a slot-based framework for structural prognostic event modeling. Specifically, inspired by the principle of factorial coding, we compress each patient's multimodal inputs into compact, modality-specific sets of mutually distinctive slots using slot attention. By leveraging these slot representations as encodings for prognostic events, our framework enables both efficient and effective modeling of complex intra- and inter-modal interactions, while also facilitating seamless incorporation of biological priors that enhance prognostic relevance. Extensive experiments on ten cancer benchmarks show that SlotSPE outperforms existing methods in 8 out of 10 cohorts, achieving an overall improvement of 2.9%. It remains robust under missing genomic data and delivers markedly improved interpretability through structured event decomposition.

翻译：组织病理学图像与基因谱的整合在改善癌症生存预测方面展现出巨大潜力。然而，由于输入数据的高维性和复杂性，现有方法通常难以高效且有效地建模模态内和模态间的交互作用。其中一个主要挑战在于捕获关键预后事件——这类事件虽数量稀少，却是观测数据复杂性的根源，并在很大程度上决定患者结局。这些事件表现为高级结构信号（如空间组织学模式或通路共激活），通常具有稀疏性、患者特异性和未标注性，导致其本质难以揭示。为解决此问题，我们提出SlotSPE——一种基于槽位的结构预后事件建模框架。具体而言，受因子编码原理启发，我们采用槽位注意力机制将每位患者的多模态输入压缩为紧凑的、模态特定的互异槽位集。通过利用这些槽位表示作为预后事件的编码，我们的框架既能够高效且有效地建模复杂的模态内和模态间交互，又便于无缝整合增强预后相关性的生物先验知识。在十个癌症基准数据集上的广泛实验表明，SlotSPE在8个队列中优于现有方法，总体性能提升2.9%。该方法在缺失基因组数据时仍保持稳健，并通过结构化事件分解实现了显著改进的可解释性。