Multimodal Survival Analysis

Follow this topic Ask about this topic

Multimodal survival analysis predicts outcomes such as cancer recurrence or death by combining time-to-event data with sources such as pathology slides, radiology, genomics, and clinical records. Common approaches encode each data type separately, then fuse representations with attention, contrastive alignment, prototypes, gating, or mixtures of experts; models often use Cox or discrete-time survival objectives. The goal is to capture complementary prognostic signals while supporting interpretation and handling missing or unevenly available modalities. A central challenge is showing that fusion improves reliable, calibrated predictions on independent clinical cohorts, since added modalities do not always help and strong risk rankings can still yield inaccurate survival probabilities.

Latest papers

47 papers4 papers in the last four weeks, down 43% on the four weeks before. 0.0% of all new papers.

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer
Apr 10, 2026Gautier Hénique, William Le, Gabriel Dayan +8Clinical Outcome Prediction Cancer Survival Prediction
MedICAL Laboratory, Polytechnique Montréeal, Montreal, Canada · Centre de recherche du CHUM (CRCHUM), Montréal, Canada · Centre Hospitalier de l’Université de Montréal (CHUM), Montreal Canada
Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
Apr 8, 2026Sambit Tarai, Ashish Chauhan, Elin Lundström +5Cancer Survival Prediction Survival Analysis
Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, SE-75185, Sweden · Molecular Imaging and Medical Physics, Department of Surgical Sciences, Uppsala University, Uppsala, SE-75185, Sweden · Antaros Medical, Mölndal, SE-43153, Sweden
MO-RiskVAE: A Multi-Omics Variational Autoencoder for Survival Risk Modeling in Multiple MyelomaMO-RiskVAE
Apr 7, 2026Zixuan Chen, Heng Zhang, YuPeng Qin +5Latent-Space Regularization Cancer Survival Prediction
Binjiang Institute of Zhejiang University · Zhejiang University · School of Medicine, Zhejiang University
Good Rankings, Wrong Probabilities: A Calibration Audit of Multimodal Cancer Survival Models
Apr 5, 2026Sajad GhawamiCancer Survival Prediction Multimodal Survival Analysis
Independent Researcher
BVFLMSP : Bayesian Vertical Federated Learning for Multimodal Survival with Privacy
Apr 2, 2026Abhilash Kar, Basisth Saha, Tanmay Sen +1Multimodal Survival Analysis Federated Learning
SQC and OR Unit, Indian Statistical Institute, Kolkata, India · Department of Statistics, Presidency University, Kolkata, India
Quantifying Cross-Modal Interactions in Multimodal Glioma Survival Prediction via InterSHAP: Evidence for Additive Signal Integration
Mar 31, 2026Iain Swift, JingHua Ye, Ruairi O'ReillyMultimodal Fusion Multimodal Survival Analysis
Department of Computer Science, Munster Technological University, Cork, Ireland
Trimodal Deep Learning for Glioma Survival Prediction: A Feasibility Study Integrating Histopathology, Gene Expression, and MRI
Mar 31, 2026Iain Swift, JingHua YeCancer Survival Prediction Multimodal Survival Analysis
Department of Computer Science, Munster Technological University, Cork, Ireland
MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modality
Mar 27, 2026Kyungwon Kim, Dosik HwangMultimodal Survival Analysis Missing-Modality Learning
Yonsei University · Korea Institute of Science and Technology
Multimodal Survival Analysis with Locally Deployable Large Language Models
Mar 23, 2026Moritz Gögl, Christopher YauMultimodal Survival Analysis Cancer Survival Prediction
University of Oxford
HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction
Mar 17, 2026Jing Dai, Chen Wu, Ming Wu +4Cancer Survival Prediction Multimodal Survival Analysis
Cancer Hospital of Dalian University of Technology, Shenyang, China · School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China · Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, China
Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective
Mar 14, 2026Junjie Zhou, Bao Xue, Meiling Wang +2Cancer Survival Prediction Multimodal Survival Analysis
The College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics · School of Computer Science, Nanjing University of Posts and Telecommunications
Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction
Mar 2, 2026Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur +4Cancer Survival Prediction Multimodal Survival Analysis
AI Technology for Life, Department of Information and Computing Sciences, Department of Biology, Utrecht University, Utrecht, The Netherlands · Department of Metabolic Diseases, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Utrecht, The Netherlands · Regenerative Medicine Center Utrecht, Utrecht, The Netherlands +3
Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction
Feb 25, 2026Toktam KhatibiCancer Survival Prediction Multimodal Survival Analysis
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction
Feb 23, 2026Ha-Anh Hoang Nguyen, Tri-Duc Phan Le, Duc-Hoang Pham +4Counterfactual Prediction Cancer Survival Prediction
University of Engineering and Technology, Hanoi Vietnam National University, Vietnam · Delft University of Technology, the Netherlands
A Contrastive Variational AutoEncoder for NSCLC Survival Prediction with Missing Modalities
Feb 19, 2026Michele Zanitti, Vanja Miskovic, Francesco Trovò +5Cancer Survival Prediction Multimodal Survival Analysis
Department of Electronic Systems, Aalborg University, Copenhagen, Denmark · Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy · Department of Medical Oncology, Istituto Nazionale dei Tumori, Milan, Italy
Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis
Nov 22, 2025Wenjing Liu, Qin Ren, Wen Zhang +2Cancer Survival Prediction Survival Analysis
Stony Brook University, Stony Brook, NY, USA · Johns Hopkins University, Baltimore, MD, USA · Brookhaven National Laboratory, Upton, NY, USA
Multimodal Feature Prototype Learning for Interpretable and Discriminative Cancer Survival Prediction
Oct 7, 2025Shuo Jiang, Zhuwen Chen, Liaoman Xu +6Computational Pathology Cancer Survival Prediction
Hangzhou Dianzi University, Hangzhou 310018, China · School of Information and Communication Technology, Griffith University, QLD 4215, Australia · Shenzhen Research Institute of Big Data, Shenzhen 518172, China +3

Apr 10, 2026J/K move · Enter open · S save

AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer

Gautier Hénique, William Le, Gabriel Dayan, Coralie Brodeur+7

MedICAL Laboratory, Polytechnique Montréeal, Montreal, Canada · Centre de recherche du CHUM (CRCHUM), Montréal, Canada · Centre Hospitalier de l’Université de Montréal (CHUM), Montreal Canada

Clinical Outcome Prediction Cancer Survival Prediction Multimodal Survival Analysis Radiomics Semi-Supervised Medical Image Segmentation

Open paper AskPDF arXiv

Extranodal extension (ENE) is an emerging prognostic factor in human papillomavirus (HPV)-associated oropharyngeal cancer (OPC), although it is currently omitted as a clinical staging criteria. Recent works have advocated for the inclusion of iENE as a prognostic marker in HPV-positive OPC staging. However, several practical limitations continue to hinder its clinical integration, including inconsistencies in segmentation, low contrast in the periphery of metastatic lymph nodes on CT imaging, and laborious manual annotations. To address these limitations, we propose a fully automated end-to-end pipeline that uses computed tomography (CT) images with clinical data to assess the status of nodal ENE and predict treatment outcomes. Our approach includes a hierarchical 3D semi-supervised segmentation model designed to detect and delineate relevant iENE from radiotherapy planning CT scans. From these segmentations, a set of radiomics and deep features are extracted to train an imaging-detected ENE grading classifier. The predicted ENE status is then evaluated for its prognostic value and compared with existing staging criteria. Furthermore, we integrate these nodal features with primary tumor characteristics in a multimodal, attention-based outcome prediction model, providing a dynamic framework for outcome prediction. Our method is validated in an internal cohort of 397 HPV-positive OPC patients treated with radiation therapy or chemoradiotherapy between 2009 and 2020. For outcome prediction at the 2-year mark, our pipeline surpassed baseline models with 88.2% (4.8) in AUC for metastatic recurrence, 79.2% (7.4) for overall survival, and 78.1% (8.6) for disease-free survival. We also obtain a concordance index of 83.3% (6.5) for metastatic recurrence, 71.3% (8.9) for overall survival, and 70.0% (8.1) for disease-free survival, making it feasible for clinical decision making.