LATIDIA · Investigación

Una red neuronal hipergráfica precisa e interpretable para la predicción de supervivencia de GBM

arXiv:2609.25088v1 Tipo de anuncio: nuevo Resumen: La predicción de supervivencia para el glioblastoma multiforme (GBM) exige modelos que sean precisos e interpretables, pero los enfoques existentes tratan estos objetivos como

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arXiv:2609.25088v1 Announce Type: new Abstract: Survival prediction for glioblastoma multiforme (GBM) demands models that are both accurate and interpretable, yet existing approaches treat these objectives as com- peting, where performant models sacrifice transparency, while interpretable models accept degraded predictive power. We argue that this trade-off is not inherent. Graph neural net- works offer a structural foundation for extracting interpretable, explainable representations without compromising discriminative ability. Furthermore, current methods typically rely on a single imaging modality, underutilizing the complementary information available across multi-modal MRI and clinical metadata. We propose a multi-modal framework that inte- grates three components to address both objectives simultaneously: (1) a sheaf hypergraph neural network that captures higher-order