Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes
This work addresses the challenge of integrating heterogeneous multimodal data for improved survival outcomes in cancer patients, representing an incremental advance in domain-specific bioinformatics.
The paper tackled the problem of handling heterogeneous and missing multimodal cancer data by proposing PARADIGM, a Graph Neural Network framework that integrates multi-resolution data to improve clinical outcome prediction, specifically showing it outperforms other models in patient survival prediction for pan-Squamous Cell Carcinomas.
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.