IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples

Abstract

In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives.

Figure 1. Representation of color deconvolution using the old and the new optical density (OD) vectors.

A: Color deconvolution using the old OD vectors. B: Color deconvolution using the new OD vectors. C: Scatter plot comparing the intensities on the complimentary image with the old OD vectors (blue) and the new OD vectors (red). D: Plot comparing the number of pixels with the intensity value of 255. An improvement between 2 to 10 fold is shown using 7 different samples. Each data plot represents an individual sample with its respective pixel count of the intensity value of 255.

Figure 2. Representative histogram profile and score of a cytoplasmic and nuclear stained image using IHC Profiler.

A: Profiling of the DAB stained cytoplasmic image sample. The histogram profile corresponds to the pixel intensity value vs. corresponding number counts of a pixel intensity. The log given below the histogram profile shows the accurate percentage of the pixels present in each zone of pixel intensity and the respective computed score. B: Profiling of the DAB stained nuclear stained image sample. The red spots on the DAB image indicate the threshold selection of the nucleus areas using the threshold function of ImageJ. The representative histogram profile corresponds to the number of pixels vs. the corresponding value at which the pixel of the respective intensity is present.

Figure 3. Flow chart demonstrating the computing steps involved in the working algorithm.

Figure 4. Impact of magnification on image scoring.

A: Analysis of a 10X image area where a significant amount of stroma and fatty tissue is present. After color deconvolution, the score assigned by IHC profiler on the DAB image was determined as low positive. B: Scoring analysis of the same tissue area where image captured was by using a 20X lens in the marked area, focusing more on the actual tumor mass resolute a score of positive. C: Scoring analysis of the same tissue area wherein the image was captured using a 40X lens, focusing more on eliminating the stromal and fatty tissue region increases the percentage of the positive pixels in the positive and high positive zones.