Structural Prognostic Event Modeling for Multimodal Cancer Survival Analysis
This work addresses the problem of improving survival prediction for cancer patients by integrating histology images and gene profiles, though it appears incremental as it builds on existing multimodal approaches with a novel method.
The paper tackled the challenge of modeling intra- and inter-modal interactions in multimodal cancer survival analysis by proposing SlotSPE, a slot-based framework for structural prognostic event modeling, which outperformed existing methods in 8 out of 10 cancer benchmarks with an overall improvement of 2.9%.
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.