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

TL;DR

This paper introduces HiGINE, a hierarchical graph-based method that analyzes cell interactions in multiplex microscopy images to improve lung cancer prognosis and risk stratification.

Contribution

The paper presents a novel hierarchical graph approach that captures complex cell interactions in TME for better survival prediction in lung cancer.

Findings

Improved risk stratification accuracy.

Robustness across datasets.

Enhanced generalizability.

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.