Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images
This work addresses the problem of analyzing cellular heterogeneity in biomedical research, particularly for astrocytomas, but it is incremental as it applies existing computational methods to new data without introducing novel techniques.
The study tackled the challenge of quantitatively comparing cellular phenotypic features in histopathology images across distinct molecular groups, specifically astrocytomas with IDH mutant vs. wildtype profiles, by proposing a self-reliant analysis framework that retrieves representative cell instances through segmentation, feature computation, and clustering.
Cellular phenotypic features derived from histopathology images are the basis of pathologic diagnosis and are thought to be related to underlying molecular profiles. Due to overwhelming cell numbers and population heterogeneity, it remains challenging to quantitatively compute and compare features of cells with distinct molecular signatures. In this study, we propose a self-reliant and efficient analysis framework that supports quantitative analysis of cellular phenotypic difference across distinct molecular groups. To demonstrate efficacy, we quantitatively analyze astrocytomas that are molecularly characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype (WT) using imaging data from The Cancer Genome Atlas database. Representative cell instances that are phenotypically different between these two groups are retrieved after segmentation, feature computation, data pruning, dimensionality reduction, and unsupervised clustering. Our analysis is generic and can be applied to a wide set of cell-based biomedical research.