Chapter title |
Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma
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Chapter number | 9 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
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Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46720-7_9 |
Pubmed ID | |
Book ISBNs |
978-3-31-946719-1, 978-3-31-946720-7
|
Authors |
Ju Han, Yunfu Wang, Weidong Cai, Alexander Borowsky, Bahram Parvin, Hang Chang, Ju Han, Yunfu Wang, Weidong Cai, Alexander Borowsky, Bahram Parvin, Hang Chang |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
Abstract |
Integrative analysis based on quantitative representation of whole slide images (WSIs) in a large histology cohort may provide predictive models of clinical outcome. On one hand, the efficiency and effectiveness of such representation 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. On the other hand, perceptual interpretation/validation of important multivariate phenotypic signatures are often difficult due to the loss of visual information during feature transformation in hyperspace. To address these issues, we propose a novel approach for integrative analysis based on cellular morphometric context, which is a robust representation of WSI, with the emphasis on tumor architecture and tumor heterogeneity, built upon cellular level morphometric features within the spatial pyramid matching (SPM) framework. The proposed approach is applied to The Cancer Genome Atlas (TCGA) lower grade glioma (LGG) cohort, where experimental results (i) reveal several clinically relevant cellular morphometric types, which enables both perceptual interpretation/validation and further investigation through gene set enrichment analysis; and (ii) indicate the significantly increased survival rates in one of the cellular morphometric context subtypes derived from the cellular morphometric context. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 3 | 33% |
Researcher | 2 | 22% |
Student > Doctoral Student | 2 | 22% |
Other | 1 | 11% |
Unknown | 1 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 4 | 44% |
Medicine and Dentistry | 2 | 22% |
Computer Science | 1 | 11% |
Physics and Astronomy | 1 | 11% |
Unknown | 1 | 11% |