Semi-automated imaging system to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer.

A semi-automated imaging system is described to quantitate estrogen and progesterone receptor immunoreactivity in human breast cancer. The system works for any conventional method of image acquisition using microscopic slides that have been processed for immunohistochemical analysis of the estrogen receptor and progesterone receptor. Estrogen receptor and progesterone receptor immunohistochemical staining produce colorimetric differences in nuclear staining that conventionally have been interpreted manually by pathologists and expressed as percentage of positive tumoral nuclei. The estrogen receptor and progesterone receptor status of human breast cancer represent important prognostic and predictive markers of human breast cancer that dictate therapeutic decisions but their subjective interpretation result in interobserver, intraobserver and fatigue variability. Subjective measurements are traditionally limited to a determination of percentage of tumoral nuclei that show positive immunoreactivity. To address these limitations, imaging algorithms utilizing both colorimetric (RGB) as well as intensity (gray scale) determinations were used to analyze pixels of the acquired image. Image acquisition utilized either scanner or microscope with attached digital or analogue camera capable of producing images with a resolution of 20 pixels /10 mu. Areas of each image were screened and the area of interest richest in tumour cells manually selected for image processing. Images were processed initially by JPG conversion of SVS scanned virtual slides or direct JPG photomicrograph capture. Following image acquisition, images were screened for quality, enhanced and processed. The algorithm-based values for estrogen receptor and progesterone receptor percentage nuclear positivity both strongly correlated with the subjective measurements (intraclass correlation: 0.77; 95% confidence interval: 0.59, 0.95) yet exhibited no interobserver, intraobserver or fatigue variability. In addition the algorithms provided measurements of nuclear estrogen receptor and progesterone receptor staining intensity (mean, mode and median staining intensity of positive staining nuclei), parameters that subjective review could not assess. Other semi-automated image analysis systems have been used to measure estrogen receptor and progesterone receptor immunoreactivity but these either have required proprietary hardware or have been based on luminosity differences alone. By contrast our algorithms were independent of proprietary hardware and were based on not just luminosity and colour but also many other imaging features including epithelial pattern recognition and nuclear morphology. These features provide a more accurate, versatile and robust imaging analysis platform that can be fully automated in the near future. Because of all these properties, our semi-automated imaging system 'adds value' as a means of measuring these important nuclear biomarkers of human breast cancer.