Format

Send to

Choose Destination
See comment in PubMed Commons below
Anal Cell Pathol. 2000;21(2):71-86.

New algorithms based on the Voronoi Diagram applied in a pilot study on normal mucosa and carcinomas.

Author information

1
Department of Pathology, The Norwegian Radium Hospital, Montebello, Oslo. jon.sudbo@rh.uio.no

Abstract

An adequate reproducibility in the description of tissue architecture is still a challenge to diagnostic pathology, sometimes with unfortunate prognostic implications. To assess a possible diagnostic and prognostic value of quantitiative tissue architecture analysis, structural features based on the Voronoi Diagram (VD) and its subgraphs were developed and tested. A series of 27 structural features were developed and tested in a pilot study of 30 cases of prostate cancer, 10 cases of cervical carcinomas, 8 cases of tongue cancer and 8 cases of normal oral mucosa. Grey level images were acquired from hematoxyline-eosine (HE) stained sections by a charge coupled device (CCD) camera mounted on a microscope connected to a personal computer (PC) with an image array processor. From the grey level images obtained, cell nuclei were automatically segmented and the geometrical centres of cell nuclei were computed. The resulting 2-dimensional (2D) swarm of pointlike seeds distributed in a flat plane was the basis for construction of the VD and its subgraphs. From the polygons, triangulations and arborizations thus obtained, 27 structural features were computed as numerical values. Comparison of groups (normal vs. cancerous oral mucosa, cervical and prostate carcinomas with good and poor prognosis) with regard to distribution in the values of the structural features was performed with Student's t-test. We demonstrate that some of the structural features developed are able to distinguish structurally between normal and cancerous oral mucosa (P = 0.001), and between good and poor outcome groups in prostatic (P = 0.001) and cervical carcinomas (P = 0.001). We present results confirming previous findings that graph theory based algorithms are useful tools for describing tissue architecture (e.g., normal versus malignant). The present study also indicates that these methods have a potential for prognostication in malignant epithelial lesions.

PMID:
11310643
PMCID:
PMC4618427
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for IOS Press Icon for PubMed Central
    Loading ...
    Support Center