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

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

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Article in Software impacts, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Pingjun ChenDepartment of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Muhammad AminuDepartment of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Siba El HusseinDepartment of Pathology, University of Rochester Medical Center, NY, USA.

Joseph D KhouryDepartment of Hematopathology, Division of Pathology and Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Jia WuDepartment of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Funding

QUANTITATIVE IMAGING BIOMARKERS OF TREATMENT RESPONSE AND PROGNOSIS IN BREAST CANCERR00CA218667 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI WU, JIA · 2020 to 2022

$747k

NCI NIH HHS R00 CA218667

6 · The paper itself

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.

Indexed as

Cell phenotypingGraph modelingSpatial analysis

Identifiers

PMID36203948

DOI10.1016/j.simpa.2021.100156

PMCPMC9534201

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.