Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks

• URL: http://arxiv.org/abs/2507.07804v1
• Date: Thu, 10 Jul 2025 14:29:48 GMT
• Title: Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
• Authors: Alba Garrido, Alejandro Almodóvar, Patricia A. Apellániz, Juan Parras, Santiago Zazo,
• Abstract summary: We introduce a multimodal deep learning framework for survival analysis capable of modeling both single and competing risks scenarios.<n>We propose SAMVAE (Survival Analysis Multimodal Variational Autoencoder), a novel deep learning architecture designed for survival prediction.
• Score: 47.19194118883552
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. Its parametric formulation enables the derivation of clinically meaningful statistics from the output distributions, providing patient-specific insights through interactive multimedia that contribute to more informed clinical decision-making and establish a foundation for interpretable, data-driven survival analysis in oncology. 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 performance compared to state-of-the-art multimodal survival models. Notably, this is the first parametric multimodal deep learning architecture to incorporate competing risks while modeling continuous time to a specific event, using both tabular and image data.

Related papers

• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations [0.6990493129893112]
  M4Survive is a novel framework that learns joint foundation model representations using efficient adapter networks.<n>By leveraging Mamba-based adapters, M4Survive enables efficient multi-modal learning while preserving computational efficiency.<n>This work underscores the potential of foundation model-driven multi-modal fusion in advancing precision oncology and predictive analytics.
  arXiv  Detail & Related papers  (2025-03-13T05:18:32Z)
• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates.<n>Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information.<n>Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals.<n>Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
  We introduce HACSurv, a survival analysis method that learns Hierarchical Archimedean Copulas structures.<n>By capturing the dependencies between risks and censoring, HACSurv improves the accuracy of survival predictions.
  arXiv  Detail & Related papers  (2024-10-19T18:52:18Z)
• A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction [9.005219442274344]
  Computer-aided cancer survival risk prediction plays an important role in the timely treatment of patients. We propose a new training method, multimodal object-level contrast learning, for cancer survival risk prediction. The survival risk predictor trained by our proposed method outperforms state-of-the-art methods on two public multimodal cancer datasets.
  arXiv  Detail & Related papers  (2024-09-03T07:36:34Z)
• M2EF-NNs: Multimodal Multi-instance Evidence Fusion Neural Networks for Cancer Survival Prediction [24.323961146023358]
  We propose a neural network model called M2EF-NNs for accurate cancer survival prediction. To capture global information in the images, we use a pre-trained Vision Transformer (ViT) model. We are the first to apply the Dempster-Shafer evidence theory (DST) to cancer survival prediction.
  arXiv  Detail & Related papers  (2024-08-08T02:31:04Z)
• Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation [7.698783025721071]
  We propose IMLSP, an Interpretable Multi-Label multi-modal deep Survival Prediction framework for predicting multiple HNC survival outcomes simultaneously. We also present Grad-TEAM, a Gradient-weighted Time-Event Activation Mapping approach specifically developed for deep survival model visual explanation.
  arXiv  Detail & Related papers  (2024-05-09T01:30:04Z)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction [43.1748594898772]
  We propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colon-related cancer survival prediction. We emphasize the unsupervised pretraining to capture the intrinsic interaction between tissue microenvironments in gigapixel whole slide images. We evaluate our approach on both TCGA colon and rectum cancer cohorts, showing that the proposed approach is competitive and outperforms state-of-the-art studies.
  arXiv  Detail & Related papers  (2023-07-22T00:59:26Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.