Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association

Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Joe W. Gray, Paul T. Spellman, Bahram Parvin

Research output: Contribution to journalArticle

61 Scopus citations

Abstract

Automated analysis of whole mount tissue sections can provide insights into tumor subtypes and the underlying molecular basis of neoplasm. However, since tumor sections are collected from different laboratories, inherent technical and biological variations impede analysis for very large datasets such as The Cancer Genome Atlas (TCGA). Our objective is to characterize tumor histopathology, through the delineation of the nuclear regions, from hematoxylin and eosin (H\E) stained tissue sections. Such a representation can then be mined for intrinsic subtypes across a large dataset for prediction and molecular association. Furthermore, nuclear segmentation is formulated within a multi-reference graph framework with geodesic constraints, which enables computation of multidimensional representations, on a cell-by-cell basis, for functional enrichment and bioinformatics analysis. Here, we present a novel method, multi-reference graph cut (MRGC), for nuclear segmentation that overcomes technical variations associated with sample preparation by incorporating prior knowledge from manually annotated reference images and local image features. The proposed approach has been validated on manually annotated samples and then applied to a dataset of 377 Glioblastoma Multiforme (GBM) whole slide images from 146 patients. For the GBM cohort, multidimensional representation of the nuclear features and their organization have identified 1) statistically significant subtypes based on several morphometric indexes, 2) whether each subtype can be predictive or not, and 3) that the molecular correlates of predictive subtypes are consistent with the literature.

Original languageEnglish (US)
Article number6374258
Pages (from-to)670-682
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number4
DOIs
StatePublished - Apr 15 2013

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Keywords

  • Molecular pathology
  • nuclear segmentation
  • subtyping
  • tumor histopathology

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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