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

Biomolecules ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
János Bencze ◽  
Máté Szarka ◽  
Balázs Kóti ◽  
Woosung Seo ◽  
Tibor G. Hortobágyi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Statistical Significance ◽  
Reference Method ◽  
Tissue Sections ◽  
Quantitative Scoring ◽  
Analysis Platform ◽  
Experimental Group

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.

Abstract 4042: Quantifying immune oncology markers across multiple tissue sections with digital image analysis

2016 ◽  
Author(s):  
Ciara A. Martin ◽  
Joshua Black ◽  
Nathan T. Martin ◽  
Logan Cerkovnik ◽  
Jasmeet Bajwa ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Tissue Sections ◽  
Immune Oncology

scholarly journals Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0245638
Author(s):  
Sripad Ram ◽  
Pamela Vizcarra ◽  
Pamela Whalen ◽  
Shibing Deng ◽  
C. L. Painter ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Large Scale ◽  
Digital Image Analysis ◽  
Tumor Resection ◽  
Automated Image Analysis ◽  
Current Standard ◽  
Tissue Sections ◽  
Slide Image ◽  
Standard Of Practice

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

scholarly journals Pixelwise H-score: a novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

2021 ◽  
Author(s):  
Sripad Ram ◽  
Pamela Vizcarra ◽  
Pamela Whalen ◽  
Shibing Deng ◽  
CL Painter ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Large Scale ◽  
Digital Image Analysis ◽  
Tumor Resection ◽  
Automated Image Analysis ◽  
Current Standard ◽  
Tissue Sections ◽  
Slide Image ◽  
Standard Of Practice

ABSTRACTImmunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

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