Work

CellSpatialGraph: Integrate hierarchical phenotyping and graph modeling to characterize spatial architecture in tumor microenvironment on digital pathology

Year: 2021

Type: article

Abstract: We present CellSpatialGraph, an integrated clustering and graph-based framework, to investigate the cellular spatial structure. Due to the lack of a clear understanding of the cell subtypes in the tumor microenvironment, unsupervised learning is applied to uncover cell phenotypes. Then, we build local cell graphs, referred to as supercells, to model the cell-to-cell relationships at a local scale. After that, we apply clustering again to identify the subtypes of supercells. In the end, we build a global graph to summarize supercell-to-supercell interactions, from which we extract features to classify different disease subtypes.

Source: Software Impacts

DOI: 10.1016/j.simpa.2021.100156

Authors: Pingjun Chen, Muhammad Aminu, Siba El Hussein, Joseph D. Khoury, Jia Wu

Institutions: The University of Texas MD Anderson Cancer Center, University of Rochester Medicine

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $250

Funders: National Institutes of Health, National Cancer Institute