Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

“…Additionally, a semisupervised or unsupervised learning strategy might be an option to make use of a large number of unannotated cells. 63 , 64 Third, as a real-world study, the data quality from different institutions was very heterogeneous. Many factors may affect the quality and consistency of acquired images, for example, different protocols in glass slide preparations, varied tissue persevering periods/conditions, and different protocols for H&E stains.…”

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

“…We conduct the benchmark experiment on lymphoid neoplasms to test the proposed framework’s performance in diagnosing three hematological malignancy subtypes [ 16 ]. We compare with three cell-level graph-based algorithms, including the Global Cell Graph (GCG) [ 9 ], Local Cell Graph (LCG) [ 10 ], and FLocK [ 11 ].…”

“…CellSpatialGraph is an open-source graph-based cell spatial analysis framework that provides a modularized pipeline to study the cellular spatial patterns to advance our understanding of intratumoral heterogeneity. This framework is among the first to integrate local and global graph approaches to interrogate cellular patterns within TME, and demonstrates superior performance in the diagnosis of lymphoid neoplasms [ 16 ]. Hereby, we hypothesize that the proposed design can overcome the limitations inherent in solely adopting either the global or local graph approaches, and conduct a more robust profiling intratumoral heterogeneity.…”