Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry

Abstract

Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen’s kappa, and overall agreement was poor within every experimental group using Fleiss’ kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.

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“…The images were acquired using Olympus I × 71 (Olympus, Japan). The immunohistochemical staining score was graded according to the German semi-quantitative scoring system 52 .…”

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A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences

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“…The results obtained are promising although limited to that specific type of tissue. In [26], a Convolutional Neural Network (CNN) was employed to annotate the IHC images of neurons. The system first recognizes and crops the stained neurons with the CNN; then, it obtains the cytoplasmatic DAB signal by deconvolution, and finally, it measures the average intensity of the cytoplasm of the neurons.…”