Deep survival analysis in multimodal medical data: a parametric and probabilistic approach with competing risks

Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modality, limiting their ability to capture the complexity of tumor biology. To address this challenge, we introduce a multimodal deep learning framework for survival analysis capable of modeling both single and competing risks scenarios, evaluating the impact of integrating multiple medical data sources on survival predictions. We propose SAMVAE (Survival Analysis Multimodal Variational Autoencoder), a novel deep learning architecture designed for survival prediction that integrates six data modalities: clinical variables, four molecular profiles, and histopathological images. SAMVAE leverages modality-specific encoders to project inputs into a shared latent space, enabling robust survival prediction while preserving modality-specific information. We evaluate SAMVAE on two cancer cohorts-breast cancer and lower-grade glioma-applying tailored preprocessing, dimensionality reduction, and hyperparameter optimization. The results demonstrate the successful integration of multimodal data for both standard survival analysis and competing risks scenarios across different datasets. Our model achieves competitive predictive performance compared to state-of-the-art multimodal survival models. To the best of our knowledge, among publicly available implementations, this framework represents an early approach to integrating clinical, multi-omics, and imaging data for continuous-time competing risks. SAMVAE introduces a probabilistic framework that supports the generation of personalized survival curves from heterogeneous data sources. Its parametric formulation enables the derivation of clinically meaningful statistics from the output distributions, providing patient-specific insights through interactive multimedia. These properties position it as a methodological tool with the potential to support risk modeling and interpretable survival analysis in oncology. Importantly, its main contribution is a unified probabilistic multimodal fusion framework.