Modified Automated Scoring system for Immunohistochemical staining using commercially available low cost software for image analysis

Abstract

Background: During the past several years, there has been a rapidly escalating clinical need to perform IHC stains that require quantitative interpretation. Automated Cellular
Imaging System is used to analyze immunohistochemically stained slides, primarily for cancer-related diagnostics. Studies have shown that the device offers accuracy,
precision, and reproducibility of immunostained slide analysis exceeding that possible with manual evaluation, which was the prevailing method.
Aim of the study In this article we will demonstrate that meaningful image analysis of immunohistochemical staining studies can be performed using inexpensive, widely distributed
graphics software (Adobe Photoshop) on a personal computer. Also we will try to use a modified digital scoring system depending on the percentage of pixels that are showing a given
stain with regard to the total area of the slide. We select three sets for each antigen (one is optimally stained, one is insufficiently stained and third one is not stained one)
materials and methods Thirty digital pictures of immunohistochemically stained slides with monoclonal antibodies against different antigens, from standard quality control lab
(NORDIQC) and then were analyzed by Adobe Photoshop software, then we distribute the percent of pixels showing a giving band of the brown color into five groups, then we compare
those results in the three groups.
Results: Results showed that there was significant difference between color bands of the same tissue among optimal and insufficient staining (P<0.05), also there was significant difference between the group of slides that were optimally stained with those insufficiently stained (p<0.05), that’s to say the procedure of scoring that was done was accurate in discriminating between optimal staining and insufficient staining
Conclusion: Each slide was converted into a matrix of data that describe every pixel in the slide and by that we can compare between all slides that’s to say we convert the visual manual
evaluation into an automated objective analysis, which is the first step in establishing quantitative immunohistochemistry.