Computer-based association of the texture of expressed estrogen receptor nuclei with histologic grade using immunohistochemically-stained breast carcinomas.
OBJECTIVE
To investigate the potential correlation between estrogen receptor (ER) texture and histologic grade in breast carcinomas.


METHODS
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.


CONCLUSIONS
ER-expressed nuclei texture was found to contain important information related to histologic grade.
Kostopoulos
Spiros
S
Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Athens, Greece. skostopoulos@upatras.gr
Glotsos
Dimitris
D
Cavouras
Dionisis
D
Daskalakis
Antonis
A
Kalatzis
Ioannis
I
Georgiadis
Pantelis
P
Bougioukos
Panagiotis
P
Ravazoula
Panagiota
P
Nikiforidis
George
G
D000465:Algorithms; D001706:Biopsy; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology*; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology*; D002467:Cell Nucleus / Q000378:metabolism*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry; D016015:Logistic Models; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011960:Receptors, Estrogen / Q000032:analysis / Q000378:metabolism*; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric
D011960:Receptors, Estrogen