# PRISM: A Framework Harnessing Unsupervised Visual Representations and Textual Prompts for Explainable MACE Survival Prediction from Cardiac Cine MRI

arXiv: https://arxiv.org/abs/2508.19325 · HTML: https://mlsanity.com/paper/2508.19325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning](https://mlsanity.com/paper/2201.04676/) (2022)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts](https://mlsanity.com/paper/2109.04379/) (2021)
- [A generalizable 3D framework and model for self-supervised learning in medical imaging](https://mlsanity.com/paper/2501.11755/) (2025)
- [Towards a vision foundation model for comprehensive assessment of Cardiac MRI](https://mlsanity.com/paper/2410.01665/) (2024)
- [A versatile foundation model for cine cardiac magnetic resonance image analysis tasks](https://mlsanity.com/paper/2506.00679/) (2025)
- [Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction](https://mlsanity.com/paper/2403.11375/) (2024)
- [AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score](https://mlsanity.com/paper/2401.16190/) (2024)
- [CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis](https://mlsanity.com/paper/2505.19195/) (2025)
- [Pericoronary adipose tissue feature analysis in CT calcium score images with comparison to coronary CTA](https://mlsanity.com/paper/2401.15554/) (2024)
- [MOSCARD -- Causal Reasoning and De-confounding for Multimodal Opportunistic Screening of Cardiovascular Adverse Events](https://mlsanity.com/paper/2506.19174/) (2025)
- [CTSL: Codebook-based Temporal-Spatial Learning for Accurate Non-Contrast Cardiac Risk Prediction Using Cine MRIs](https://mlsanity.com/paper/2507.16612/) (2025)
- [Dynamic Survival Prediction using Longitudinal Images based on Transformer](https://mlsanity.com/paper/2508.09328/) (2025)

## Abstract (arXiv)

Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for Survival Modeling), a self-supervised framework that integrates visual representations from non-contrast cardiac cine magnetic resonance imaging with structured electronic health records (EHRs) for survival analysis. PRISM extracts temporally synchronized imaging features through motion-aware multi-view distillation and modulates them using medically informed textual prompts to enable fine-grained risk prediction. Across four independent clinical cohorts, PRISM consistently surpasses classical survival prediction models and state-of-the-art (SOTA) deep learning baselines under internal and external validation. Further clinical findings demonstrate that the combined imaging and EHR representations derived from PRISM provide valuable insights into cardiac risk across diverse cohorts. Three distinct imaging signatures associated with elevated MACE risk are uncovered, including lateral wall dyssynchrony, inferior wall hypersensitivity, and anterior elevated focus during diastole. Prompt-guided attribution further identifies hypertension, diabetes, and smoking as dominant contributors among clinical and physiological EHR factors.