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 multi-variate 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.
| Original language | English (US) |
|---|---|
| Title of host publication | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings |
| Publisher | Springer Verlag |
| Pages | 72-80 |
| Number of pages | 9 |
| Volume | 9900 LNCS |
| ISBN (Print) | 9783319467191 |
| DOIs | |
| State | Published - 2016 |
| Event | 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: Oct 21 2016 → Oct 21 2016 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9900 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 10/21/16 → 10/21/16 |
Fingerprint
Keywords
- Cellular morphometric context
- Cellular morphometric type
- Consensus clustering
- Gene set enrichment analysis
- Lower grade glioma
- Spatial pyramid matching
- Survival analysis
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma. / Han, Ju; Wang, Yunfu; Cai, Weidong; Borowsky, Alexander D; Parvin, Bahram; Chang, Hang.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 72-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma
AU - Han, Ju
AU - Wang, Yunfu
AU - Cai, Weidong
AU - Borowsky, Alexander D
AU - Parvin, Bahram
AU - Chang, Hang
PY - 2016
Y1 - 2016
N2 - 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 multi-variate 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.
AB - 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 multi-variate 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.
KW - Cellular morphometric context
KW - Cellular morphometric type
KW - Consensus clustering
KW - Gene set enrichment analysis
KW - Lower grade glioma
KW - Spatial pyramid matching
KW - Survival analysis
UR - http://www.scopus.com/inward/record.url?scp=84996602567&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996602567&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46720-7_9
DO - 10.1007/978-3-319-46720-7_9
M3 - Conference contribution
SN - 9783319467191
VL - 9900 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 72
EP - 80
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PB - Springer Verlag
ER -