Phenotypic Characterization of Breast Invasive Carcinoma via Transferable Tissue Morphometric Patterns learned from Glioblastoma Multiforme.
|Title||Phenotypic Characterization of Breast Invasive Carcinoma via Transferable Tissue Morphometric Patterns learned from Glioblastoma Multiforme.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Han J, Fontenay GV, Wang Y, Mao J-H, Chang H|
|Journal||Proc IEEE Int Symp Biomed Imaging|
|Date Published||2016 Apr|
Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.
|Alternate Journal||Proc IEEE Int Symp Biomed Imaging|
|PubMed Central ID||PMC4932846|
|Grant List||R01 CA184476 / CA / NCI NIH HHS / United States|