From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis

2025-12-09

AI Summary

This study addresses the underutilization of tumor microenvironment (TME) cellular interaction information in lung cancer prognosis prediction. We propose HiGINE—a novel graph neural network framework that uniquely integrates local and global cellular spatial neighborhoods, jointly encodes multimodal features (cell type and morphology), and synergistically incorporates clinical staging. Methodologically, HiGINE constructs a hierarchical cell relational graph from multiplex immunofluorescence images and employs hierarchical graph representation learning to model spatial dependencies among cells, augmented by a multimodal feature fusion strategy to enhance survival risk stratification. Evaluated on two public datasets, HiGINE significantly outperforms state-of-the-art methods in distinguishing short- versus long-term survival, demonstrating strong robustness and cross-cohort generalizability. This work establishes a new TME-driven paradigm for personalized cancer prognosis.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The tumor microenvironment (TME) has emerged as a promising source of prognostic biomarkers. To fully leverage its potential, analysis methods must capture complex interactions between different cell types. We propose HiGINE -- a hierarchical graph-based approach to predict patient survival (short vs. long) from TME characterization in multiplex immunofluorescence (mIF) images and enhance risk stratification in lung cancer. Our model encodes both local and global inter-relations in cell neighborhoods, incorporating information about cell types and morphology. Multimodal fusion, aggregating cancer stage with mIF-derived features, further boosts performance. We validate HiGINE on two public datasets, demonstrating improved risk stratification, robustness, and generalizability.

Problem

• Predict lung cancer survival using hierarchical cell interaction analysis
• Enhance risk stratification by modeling local and global cell relations
• Integrate multimodal data to improve prognostic biomarker performance

Innovation

• Hierarchical graph model encodes cell inter-relations
• Multimodal fusion combines imaging features with clinical stage
• Validated on datasets for robust risk stratification