TY - GEN
T1 - Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images
AU - Roy, Mousumi
AU - Wang, Fusheng
AU - Teodoro, George
AU - Vega, Jose Velazqeuz
AU - Brat, Daniel
AU - Kong, Jun
N1 - Funding Information:
Mousumi Roy, and Fusheng Wang are with the Stony Brook University, Dept. of Computer Science, Stony Brook, NY 11794 ({mousumi.roy, fusheng.wang}@stonybrook.edu); George Teodoro is with the University of Brasília, Dept. of Computer Science, Brasília, DF, Brazil (glmteodoro@gmail.com); Daniel Brat is with the Northwestern University, Dept. of Pathology, Chicago, IL 60611 (daniel.brat@northwestern.edu); Jose Velazqeuz Vega and Jun Kong are with the Emory University, Dept. of Biomedical Informatics, Atlanta, GA 30322 ({jose.enrique.velazquez.vega, jun.kong}@emory.edu); Funded by NIH K25CA181503, NSF ACI 1443054 and IIS 1350885, and CNPq; The studies involving human subjects were approved by the Emory University IRB.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056639895&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056639895&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8513157
DO - 10.1109/EMBC.2018.8513157
M3 - Conference contribution
C2 - 30441386
AN - SCOPUS:85056639895
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4644
EP - 4647
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -
