AIMS: The authors have previously described quantitative, computer-assisted analysis of oestrogen receptor status in immuno-histochemically stained sections in patients with primary breast cancer. The aim of this study was to validate the aforementioned system against the commonly used methods of assessing oestrogen receptor status. METHODS: Paraffin embedded sections from 156 patients with primary breast carcinoma were stained with anti-alpha-oestrogen receptor monoclonal antibody (1D5) using a standard immunohistochemical protocol. Images from 10 high-powered fields were captured from each section using a digital camera mounted on a microscope and analyzed using Adobe Photoshop image analysis software. A nuclear mask was obtained by digitally selecting the nuclear area. Staining intensity in the nuclear mask was then analyzed using red-scale absorption characteristics. Manual assessment of oestrogen receptor status was performed through counting the percentages of cells that are positive from 200 randomly sampled nuclei from ten high powered fields HPF. Cut off value for positivity was taken as 10%. Cytosolic oestrogen receptor concentration was measured through enzyme immunisation. Cut off value for ER positivity was taken as 200 fmol/g (wet tissue). RESULTS: One hundred and fifty-six sections were studied of which 41 were ER negative. Median percentage positivity in the remainder was 90% (17-100) by manual assessment. The median red scale value was 108 (58-156). A close correlation was observed between median optical density of the nuclear mask and percentage positivity assessed manually (P<0.0001). There was a significant correlation between the optical density of the nuclear mask and cytosolic oestrogen receptor concentration (P<0.001). CONCLUSION: Oestrogen receptor positivity can be accurately assessed through digital image analysis. This process offers objective data regarding the amount of oestrogen receptors within the nuclei as well as the percentage of nuclei, which express oestrogen receptors.
AIMS: The authors have previously described quantitative, computer-assisted analysis of oestrogen receptor status in immuno-histochemically stained sections in patients with primary breast cancer. The aim of this study was to validate the aforementioned system against the commonly used methods of assessing oestrogen receptor status. METHODS:Paraffin embedded sections from 156 patients with primary breast carcinoma were stained with anti-alpha-oestrogen receptor monoclonal antibody (1D5) using a standard immunohistochemical protocol. Images from 10 high-powered fields were captured from each section using a digital camera mounted on a microscope and analyzed using Adobe Photoshop image analysis software. A nuclear mask was obtained by digitally selecting the nuclear area. Staining intensity in the nuclear mask was then analyzed using red-scale absorption characteristics. Manual assessment of oestrogen receptor status was performed through counting the percentages of cells that are positive from 200 randomly sampled nuclei from ten high powered fields HPF. Cut off value for positivity was taken as 10%. Cytosolic oestrogen receptor concentration was measured through enzyme immunisation. Cut off value for ER positivity was taken as 200 fmol/g (wet tissue). RESULTS: One hundred and fifty-six sections were studied of which 41 were ER negative. Median percentage positivity in the remainder was 90% (17-100) by manual assessment. The median red scale value was 108 (58-156). A close correlation was observed between median optical density of the nuclear mask and percentage positivity assessed manually (P<0.0001). There was a significant correlation between the optical density of the nuclear mask and cytosolic oestrogen receptor concentration (P<0.001). CONCLUSION: Oestrogen receptor positivity can be accurately assessed through digital image analysis. This process offers objective data regarding the amount of oestrogen receptors within the nuclei as well as the percentage of nuclei, which express oestrogen receptors.
Authors: Marios A Gavrielides; Brandon D Gallas; Petra Lenz; Aldo Badano; Stephen M Hewitt Journal: Arch Pathol Lab Med Date: 2011-02 Impact factor: 5.534
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