Computer-based association of the texture of expressed estrogen receptor nuclei with histologic grade using immunohistochemically-stained breast carcinomas

Anal Quant Cytol Histol. 2009 Aug;31(4):187-96.

Abstract

Objective: To investigate the potential correlation between estrogen receptor (ER) texture and histologic grade in breast carcinomas.

Study design: Clinical material comprised 96 biopsies of infiltrative ductal carcinomas that were hematoxylin-eosin (H-E) and immunohistochemically (IHC) stained. H-E-stained specimens were used for tumor grading, and IHC-stained specimens were analyzed for ER-status estimation. Spearman's correlation test was used to estimate the relation between histologic grade and both the physician's ER-status assessment and a computer system's ER-status evaluation. Moreover, a pattern recognition system was developed that takes as input textural features extracted from ER-expressed nuclei and outputs the grade of the tumor. The system was evaluated using an external cross-validation procedure in order to assess its generalization to new cases.

Results: Spearman's correlation revealed that the histologic grading was inversely related to both the physician's ER-status assessment and to the computer system's ER-status evaluation. The pattern recognition system was able to predict histologic grade with 95.2% accuracy. Important textural nuclear features were proven--the skewness, the angular second moment and the sum of entropy.

Conclusion: ER-expressed nuclei texture was found to contain important information related to histologic grade.

MeSH terms

  • Algorithms
  • Biopsy
  • Breast Neoplasms / diagnosis*
  • Breast Neoplasms / metabolism
  • Breast Neoplasms / pathology*
  • Carcinoma, Ductal, Breast / diagnosis*
  • Carcinoma, Ductal, Breast / metabolism
  • Carcinoma, Ductal, Breast / pathology*
  • Cell Nucleus / metabolism*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Immunohistochemistry
  • Logistic Models
  • Neural Networks, Computer
  • Pattern Recognition, Automated
  • Receptors, Estrogen / analysis
  • Receptors, Estrogen / metabolism*
  • Reproducibility of Results
  • Statistics, Nonparametric

Substances

  • Receptors, Estrogen