Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer.
The expression of the HER-2/neu (HER2) gene, a member of the epidermal growth factor receptor family, has been shown to be a valuable prognostic indicator for breast cancer. However, interobserver variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective evaluation of HER2 expression. In this manuscript, we present an automated method for the quantitative assessment of HER2 using digital microscopy. The method processes microscopy images from tissue slides with a multistage algorithm, including steps of color pixel classification, nuclei segmentation, and cell membrane modeling, and extracts quantitative, continuous measures of cell membrane staining intensity and completeness. A minimum cluster distance classifier merges the features to classify the slides into HER2 categories. An evaluation based on agreement analysis with pathologist-derived HER2 scores, showed good agreement with the provided truth. Agreement varied within the different classes with highest agreement (up to 90%) for positive (3+) slides, and lowest agreement (72%-78%) for equivocal (2+) slides which contained ambiguous scoring. The developed automated method has the potential to be used as a computer aid for the immunohistochemical evaluation of HER2 expression with the objective of increasing observer reproducibility.
Masmoudi
Hela
H
Department of Electrical and Computer Engineering, The George Washington University, Washington, DC 20052, USA.
Hewitt
Stephen M
SM
Petrick
Nicholas
N
Myers
Kyle J
KJ
Gavrielides
Marios A
MA
D000465:Algorithms; D001331:Automation; D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000737:chemistry* / Q000473:pathology; D002467:Cell Nucleus / Q000648:ultrastructure; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007150:Immunohistochemistry / Q000379:methods*; D016014:Linear Models; D008853:Microscopy; D015588:Observer Variation; D012372:ROC Curve; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis*; D015203:Reproducibility of Results
D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human