From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis
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Abstract (arXiv)
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.
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References · 9
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2019Strategies for Pre-training Graph Neural Networkscited by 971
- 2019Self-Attention Graph Poolingcited by 421
- 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 384
- 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
- 2021Hierarchical Graph Representations in Digital Pathologycited by 59
- 2019CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Imagescited by 57
- 2023AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Imagescited by 18
- 2024xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancercited by 1
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