Augmented cell-graphs for automated cancer diagnosis

Cigdem Demir, Sakir Gultekin, Bülent Yener

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

Summary: This work reports a novel computational method based on augmented cell-graphs (ACG), which are constructed from low-magnification tissue images for the mathematical diagnosis of brain cancer (malignant glioma). An ACG is a simple, undirected, weighted and complete graph in which a node represents a cell cluster and an edge between a pair of nodes defines a binary relationship between them. Both the nodes and the edges of an ACG are assigned weights to capture more information about the topology of the tissue. In this work, the experiments are conducted on a dataset that is comprised of 646 human brain biopsy samples from 60 different patients. It is shown that the ACG approach yields sensitivity of 97.53% and specificities of 93.33 and 98.15% (for the inflamed and healthy, respectively) at the tissue level in glioma diagnosis.

Original languageEnglish (US)
JournalBioinformatics
Volume21
Issue numberSUPPL. 2
DOIs
StatePublished - Sep 2005

Fingerprint

Cancer
Tissue
Brain
Cell
Graph in graph theory
Neoplasms
Biopsy
Glioma
Computational methods
Vertex of a graph
Topology
Weighted Graph
Simple Graph
Undirected Graph
Brain Neoplasms
Complete Graph
Computational Methods
Specificity
Binary
Weights and Measures

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Biochemistry
  • Molecular Biology
  • Computational Mathematics
  • Statistics and Probability

Cite this

Augmented cell-graphs for automated cancer diagnosis. / Demir, Cigdem; Gultekin, Sakir; Yener, Bülent.

In: Bioinformatics, Vol. 21, No. SUPPL. 2, 09.2005.

Research output: Contribution to journalArticle

Demir, Cigdem ; Gultekin, Sakir ; Yener, Bülent. / Augmented cell-graphs for automated cancer diagnosis. In: Bioinformatics. 2005 ; Vol. 21, No. SUPPL. 2.
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