Semi-automated imaging system to quantitate Her-2/neu membrane receptor immunoreactivity in human breast cancer

Immunocytochemical methods for quantitating Her-2/neu immunoreactivity rest on subjective semi-quantitative interpretations with resulting interobserver, intraobserver, and fatigue variability.

To standardize and quantitate measurements of Her-2/neu immunoreactivity, we created epithelial-recognition and specific membrane-recognition algorithms, which could image breast cancer cells against a background of stroma, compartmentalize the cancer cell into nucleus, cytoplasm and membrane, and quantitate the degree of Her-2/neu membrane immunoreactivity based on both gray scale intensity and RGB colorimetric determinations. Image acquisition utilized either scanner or microscope with attached camera with a resolution of 20 pixels/10 microm. Areas of 150 whole slides were screened and the regions of interest manually selected for image processing. Three hundred TMA cores were directly processed. Images were acquired by jpg conversion of svs virtual slides or direct jpg photomicrograph capture. Images were then assessed for quality and processed.

The digital algorithms successfully created a semi-automated imaging system whose algorithm-based ordinal values for Her-2/neu both strongly correlated with the subjective measurements (intraclass correlation: 0.84; 95% confidence interval: 0.79-0.89) yet exhibited no run variability. In addition, the algorithms generated immunocytochemical measurements of Her-2/neu on an expanded continuous scale, which more reliably distinguished true Her-2/neu positivity from true Her-2/neu negativity (determined by FISH) than subjective or algorithmic ordinal scale measurements. Furthermore, the continuous scale measurements could better resolve different levels of Her-2/neu overexpression than either subjective or algorithmic ordinal interpretation. Other semi-automated analysis systems have been used to measure Her-2/neu and other cellular immunoreactivities, but these either have required proprietary hardware or have been based on luminosity differences alone. In contrast, our algorithms are independent of proprietary hardware and are based on not just luminosity but also many other imaging properties including epithelial recognition and membrane morphology.

These features provide a more accurate, versatile, and robust imaging analysis platform.