Entropy measures for quantifying complexity in digital pathology and spatial omics
Xiao Li, Xuehan Ren, Raghavan Venugopal
Abstract
Entropy, a cornerstone of information theory, quantifies disorder, heterogeneity, and complexity in biological systems. This review explores its use in digital pathology and spatial omics, focusing on how entropy captures tissue architecture, spatial heterogeneity, and cellular organization. This review synthesizes entropy's theoretical foundations, spanning classical and spatially-aware metrics, and highlights its applications in mapping tissue heterogeneity, profiling microenvironments, and informing biomarker discovery through AI/ML. Key challenges include computational scalability, interpretability, and the need for standardization. We also discuss future directions, including graph-based entropy for cell networks, dynamic entropy for disease progression, and integrative approaches across molecular and spatial data. By merging theoretical precision with clinical applications, entropy-based methods offer promising tools for advancing precision medicine and personalized treatment strategies.