Computer-assisted assessment of the Human Epidermal Growth Factor Receptor 2 immunohistochemical assay in imaged histologic sections using a membrane isolation algorithm and quantitative analysis of positive controls

Bonnie H Hall¹^,2^,3^,4 , Monica Ianosi-Irimie⁴^,5 , Parisa Javidian⁴^,5 , Wenjin Chen²^,3^,4 , Shridar Ganesan³^,4^,6 and David J Foran²^,3^,4^,5

¹Graduate School for the Biomedical Sciences, UMDNJ, 675 Hoes Lane, Piscataway, New Jersey, USA

²Center for Biomedical Imaging and Informatics, 675 Hoes Lane, Piscataway, New Jersey, USA

³The Cancer Institute of New Jersey, 195 Little Albany Street, New Brunswick, New Jersey, USA

⁴Robert Wood Johnson Medical School, UMDNJ, 675 Hoes Lane, Piscataway, New Jersey, USA

⁵Department of Pathology and Laboratory Medicine, Robert Wood Johnson Medical School, UMDNJ, 1 Robert Wood Johnson Place, New Brunswick , New Jersey, USA

⁶Department of Medicine and Pharmacology, Robert Wood Johnson Medical School, UMDNJ, 1 Robert Wood Johnson Place, New Brunswick , New Jersey, USA

author email corresponding author email

BMC Medical Imaging 2008, 8:11doi:10.1186/1471-2342-8-11


Published:	5 June 2008

Abstract

Background

Breast cancers that overexpress the human epidermal growth factor receptor 2 (HER2) are eligible for effective biologically targeted therapies, such as trastuzumab. However, accurately determining HER2 overexpression, especially in immunohistochemically equivocal cases, remains a challenge. Manual analysis of HER2 expression is dependent on the assessment of membrane staining as well as comparisons with positive controls. In spite of the strides that have been made to standardize the assessment process, intra- and inter-observer discrepancies in scoring is not uncommon. In this manuscript we describe a pathologist assisted, computer-based continuous scoring approach for increasing the precision and reproducibility of assessing imaged breast tissue specimens.

Methods

Computer-assisted analysis on HER2 IHC is compared with manual scoring and fluorescence in situ hybridization results on a test set of 99 digitally imaged breast cancer cases enriched with equivocally scored (2+) cases. Image features are generated based on the staining profile of the positive control tissue and pixels delineated by a newly developed Membrane Isolation Algorithm. Evaluation of results was performed using Receiver Operator Characteristic (ROC) analysis.

Results

A computer-aided diagnostic approach has been developed using a membrane isolation algorithm and quantitative use of positive immunostaining controls. By incorporating internal positive controls into feature analysis a greater Area Under the Curve (AUC) in ROC analysis was achieved than feature analysis without positive controls. Evaluation of HER2 immunostaining that utilized membrane pixels, controls, and percent area stained showed significantly greater AUC than manual scoring, and significantly less false positive rate when used to evaluate immunohistochemically equivocal cases.

Conclusion

It has been shown that by incorporating both a membrane isolation algorithm and analysis of known positive controls a computer-assisted diagnostic algorithm was developed that can reproducibly score HER2 status in IHC stained clinical breast cancer specimens. For equivocal scoring cases, this approach performed better than standard manual evaluation as assessed by ROC analysis in our test samples. Finally, there exists potential for utilizing image-analysis techniques for improving HER2 scoring at the immunohistochemically equivocal range.