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

2025/12/09 by Schüllerqvist, Olle Edgren, Baumann, Jens, Lindblad, Joakim +4
Biochemistry, Genetics and Molecular Biology · Computer Science · #AI in cancer detection #Cell #Cell Image Analysis Techniques #Cell type #Immunofluorescence #Leverage (statistics) #Lung cancer #Multiplex #Single-cell and spatial transcriptomics #Stage (stratigraphy) #Tumor microenvironment

paper · open access · doi:10.48550/arxiv.2512.08572

published in Diva portal (Dalarna University Library)

openalex publication_date 2025/12/09 · openalex created_date 2025/12/11 · openalex updated_date 2026/07/28

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.

Citations

• xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
• Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
• AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images
• Hierarchical Graph Representations in Digital Pathology
• Explainability in Graph Neural Networks: A Taxonomic Survey
• CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
• Strategies for Pre-training Graph Neural Networks
• Self-Attention Graph Pooling
• How Powerful are Graph Neural Networks?

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