From Cells to Survival: Hierarchical Analysis of Cell Inter-Relations in Multiplex Microscopy for Lung Cancer Prognosis
This work addresses the need for better prognostic biomarkers in lung cancer by enhancing risk stratification, though it appears incremental as it builds on existing graph-based methods for tumor microenvironment analysis.
The paper tackled the problem of predicting lung cancer patient survival by analyzing complex cell interactions in multiplex microscopy images, resulting in improved risk stratification and generalizability across two public datasets.
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.