Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma

Ju Han, Yunfu Wang, Weidong Cai, Alexander D Borowsky, Bahram Parvin, Hang Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages72-80
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
StatePublished - 2016
Event1st 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 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st 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
CountryGreece
CityAthens
Period10/21/1610/21/16

Fingerprint

Tumors
Signature
Genes
Histology
Tumor
Hyperspace
Atlas
Cell
Predictive Model
Pyramid
Fixation
Cancer
Genome
Context
Gene
Experimental Results
Interpretation

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

Han, J., Wang, Y., Cai, W., Borowsky, A. D., Parvin, B., & Chang, H. (2016). Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 72-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_9

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 proceedingConference contribution

Han, J, Wang, Y, Cai, W, Borowsky, AD, Parvin, B & Chang, H 2016, Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9900 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 72-80, 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, 10/21/16. https://doi.org/10.1007/978-3-319-46720-7_9
Han J, Wang Y, Cai W, Borowsky AD, Parvin B, Chang H. Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma. In 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)). https://doi.org/10.1007/978-3-319-46720-7_9
Han, Ju ; Wang, Yunfu ; Cai, Weidong ; Borowsky, Alexander D ; Parvin, Bahram ; Chang, Hang. / Integrative analysis of cellular morphometric context reveals clinically relevant signatures in lower grade glioma. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. pp. 72-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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