Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes

TL;DR

This paper introduces PARADIGM, a Graph Neural Network framework that integrates multimodal, heterogeneous data at multiple scales to improve survival prediction in pan-Squamous Cell Carcinomas, demonstrating superior performance over existing models.

Contribution

The paper presents a novel GNN-based framework that effectively combines multi-resolution, multimodal data for clinical outcome prediction, addressing challenges of data heterogeneity and missing modalities.

Findings

Multimodal GNN outperforms other models in survival prediction.

Converging data modalities provides a more comprehensive disease view.

Framework successfully integrates heterogeneous data at multiple scales.

Abstract

Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especially with missing modalities. We propose PARADIGM, a Graph Neural Network (GNN) framework that learns from multimodal, heterogeneous datasets to improve clinical outcome prediction. PARADIGM generates embeddings from multi-resolution data using foundation models, aggregates them into patient-level representations, fuses them into a unified graph, and enhances performance for tasks like survival analysis. We train GNNs on pan-Squamous Cell Carcinomas and validate our approach on Moffitt Cancer Center lung SCC data. Multimodal GNN outperforms other models in patient survival prediction. Converging individual data modalities across varying scales provides a more insightful disease view. Our solution aims to understand the patient's circumstances comprehensively, offering insights on heterogeneous data integration and the benefits of converging maximum data views.