Structural Prognostic Event Modeling for Multimodal Cancer Survival Analysis

• URL: http://arxiv.org/abs/2512.01116v1
• Date: Sun, 30 Nov 2025 22:24:09 GMT
• Title: Structural Prognostic Event Modeling for Multimodal Cancer Survival Analysis
• Abstract summary: SlotSPE is a slot-based framework for structural prognostic event modeling.<n>We compress each patient's multimodal inputs into compact, modality-specific sets of mutually distinctive slots.<n>Our framework enables both efficient and effective modeling of complex intra- and inter-modal interactions.
• Score: 31.582890865489574
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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