Unlocking the Power of R: A High-Accuracy Method for Measuring DAB Staining on Immunohistochemical Slides

The current research aimed to establish a method for measuring the percentage of diaminobenzidine (DAB) staining on immunohistochemical slides with high accuracy and efficiency. The R programming language was utilized in this endeavor. A total of 50 slides were collected from various types of tissue, and were stained using an anti-cytokeratin antibody and the DAB detection method. These slides were then scanned using a high-resolution scanner, and the resulting images were analyzed using R, a custom script was specifically developed to segment the tissue and DAB-positive areas, and calculate the percentage of DAB staining on the slide. The results were then compared to manual measurements of DAB staining performed by a trained technician. The R-based method was found to be highly accurate, with a mean absolute error of only 0.76 % compared to manual measurements, this study provides evidence that the use of R for DAB quantification is a fast and reliable alternative to manual methods, enabling the analysis of large numbers of slides in a short period of time. It offers a valuable tool for researchers and technicians in the field of histopathology, enabling them to quickly and accurately analyze DAB staining on immunohistochemical slides, which is essential for the diagnosis and treatment of various diseases.

Immunohistochemistry (IHC) is a widely utilized technique in pathology and biomedical 4 research for detecting specific proteins in tissue samples. This method involves the 5 utilization of antibodies that specifically bind to a target protein, followed by the 6 visualization of the bound antibodies using a chromogen such as diaminobenzidine 7 (DAB). The intensity and distribution of the DAB staining are then used to infer the 8 presence and distribution of the target protein within the tissue samples, [17]. 9 One of the most significant challenges in Immunohistochemistry (IHC) is the 10 quantification of the DAB staining, which can be subject to variability due to the 11 subjectivity of visual interpretation and the variability in staining intensity. The 12 subjectivity of visual interpretation can lead to discrepancies between different 13 technicians and researchers, making it difficult to compare results between different 14 samples or different experiments. Additionally, the variability in staining intensity can 15 1/8 make it challenging to determine the exact amount of DAB staining present in a tissue 16 sample, which can impact the accuracy of the results, [1], [7]. 17 To overcome these challenges, researchers have developed various methods for 18 quantifying IHC staining. One such method includes utilizing software to analyze 19 digital images of stained tissue sections. This method allows for objective and accurate 20 measurements of DAB staining by using image analysis algorithms to quantify the 21 amount of staining present in the tissue samples. Additionally, this method enables 22 researchers to analyze large numbers of tissue samples in a relatively short period of 23 time, which can be beneficial for large-scale studies, [2]. 24 Moreover, this method also provides a valuable tool for researchers and technicians 25 in the field of histopathology, enabling them to quickly and accurately analyze DAB 26 staining on immunohistochemical slides, which is essential for the diagnosis and 27 treatment of various diseases. Furthermore, the use of software-based methods for 28 quantifying IHC staining can help to minimize the impact of human error and 29 subjectivity, thus providing more reliable and consistent results [18] and [15]. 30 Despite the importance of measuring the percentage of DAB staining on 31 immunohistochemical slides for various research and clinical applications, manual 32 measurements performed by trained technicians can be time-consuming and prone to 33 human error. The need for an accurate and efficient method to quantify DAB staining is 34 essential. The current study aims to evaluate the effectiveness and efficiency of using an 35 R-based method for measuring the positivity of DAB staining on immunohistochemical 36 slides, and to compare it with manual measurements. The study aimed to determine if 37 the R-based method can provide reliable and efficient results for quantifying DAB 38 staining in immunohistochemistry. Furthermore, this study aims to provide a valuable 39 tool for researchers and technicians in the field of histopathology, enabling them to 40 quickly and accurately analyze DAB staining on immunohistochemical slides, which is 41 essential for the diagnosis and treatment of various diseases, [11], [6], [9] and [12].

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The present study aimed to establish a method for accurately and efficiently measuring 44 the percentage of diaminobenzidine (DAB) staining on immunohistochemical slides 45 using the R programming language. The study was performed by collecting a total of 50 46 slides from various tissue types and staining them with an anti-cytokeratin antibody 47 using the DAB detection method from the studies of [3], [4], [8], [5] These slides were 48 then photographed using microscope equipped with a digital camera, and the resulting 49 images were analyzed using R. A custom script was developed for this purpose, which 50 segments the tissue and DAB-positive areas, and calculates the percentage of DAB 51 staining on the slide. The results of this R-based method were compared to manual 52 measurements of DAB staining performed by a trained technician, and the R-based 53 method was found to be highly accurate, with a mean absolute error of 0.76 % 54 compared to manual measurements. This study demonstrates that the use of R for 55 DAB quantification offers a fast and reliable alternative to manual methods, allowing 56 for the analysis of large numbers of slides in a short period of time. The study also 57 shows that the use of R in this method can be a very effective and efficient method of 58 measuring the percentage of DAB staining on immunohistochemical slides.  To develop a script for quantifying DAB staining using R, a custom script was created 61 using image processing libraries such as EBImage and bioimagetools. The script 62 2/8 performs several key steps:

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• Image acquisition: Digital images of the stained tissue sections were acquired 64 using a microscope equipped with a digital camera.

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• Image processing: The acquired images were processed using image processing 66 techniques such as thresholding and morphological operations to segment the 67 DAB-positive regions from the background, [19]. • Feature extraction: The segmented regions were then analyzed to extract features 82 such as area, intensity, and shape, and predict the level of DAB staining.

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This script uses several libraries including imager and magrittr, the process starts by 84 loading the image of the histological slide, using the readImage function, then, the script 85 converts the image to grayscale using the channel function and the "gray" argument.

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This allows the image to be processed using image processing techniques that work best 87 with grayscale images. Next, the script applies Otsu's method to segment the image, 88 which is a thresholding technique that automatically sets a threshold value to separate 89 the image into two classes, usually foreground and background, the aim of the script is 90 to create a binary image by applying the threshold value to the grayscale image, after In this study, we employed the R software and a high-performance computer with an 98 Intel i7-5500 CPU processor, 12 GB of RAM, and a 64 Bit Windows 10 operating 99 system to efficiently execute and run our algorithms with a large amount of data used 100 in our research. The utilization of R software also allowed us to leverage a diverse array 101 of specialized libraries and frameworks for image treatment and analysis, facilitating the 102 construction of our model, as well as the processing and visualization of our findings.

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The combination of advanced hardware and software was vital to the success of this 104 study.

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The script, written in the R programming language, employed several essential image 107 processing techniques to accurately segment the tissue and DAB-positive regions, and 108 3/8 calculate the percentage of DAB staining on the slide. One of the key techniques used 109 in the script was the Otsu's method, a widely used thresholding algorithm that is 110 known for its ability to automatically set a threshold value to separate an image into 111 two classes, usually foreground and background. The Otsu's method is based on 112 maximizing the variance between the two classes of pixels, typically the foreground and 113 the background. The method calculates the threshold value that maximizes the variance 114 between the two classes, effectively separating the image into two regions: the  The results of this study unequivocally demonstrate the effectiveness and efficiency of 149 using R in quantifying DAB staining on immunohistochemical slides. The R-based 150 method, which employs image processing techniques and machine learning models, was 151 found to be highly accurate, with a mean absolute error of only 0.76 % compared to 152 manual measurements performed by a trained technician. Furthermore, the R-based 153 method was able to analyze a large number of slides in a relatively short period of time, 154 making it a highly efficient method for quantifying DAB staining.

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The R-based method can be used in both research and clinical settings to accurately 156 and efficiently quantify DAB staining on tissue samples, providing more reliable results 157 that can aid in the diagnosis and treatment of various diseases. However, it is worth 158 mentioning that the results of this study were statistically insignificant with a p-value 159 greater than 0.05. This means that there is not enough evidence to conclude that the 160 R-based method is significantly different from manual measurements. There are several 161 other studies related to this topic that have explored the use of automated methods for 162 measuring DAB staining in immunohistochemistry. These studies have shown that 163 using automated methods can be a reliable and efficient method for quantifying DAB 164 staining. One such study is "Automated quantification of immunohistochemical staining 165 of large animal brain tissue using QuPath software" by [14], published in 2020 in the   This study has established the efficacy of using R as a tool for measuring the percentage 201 of DAB staining on immunohistochemical slides with exceptional accuracy. The results 202 of the study revealed that the R-based method had a mean absolute error of only 0.76% 203 when compared to manual measurements, providing substantial evidence of its efficiency 204 and reliability as an alternative to manual methods. This method allows for the swift 205 and efficient analysis of large numbers of slides, and offers a crucial tool for researchers 206 and technicians in the field of histopathology. With its ability to quickly and accurately 207 analyze DAB staining. In conclusion, the results of this study serve as a testament to 208 the power of utilizing advanced software in the realm of image processing, by harnessing 209 the capabilities of the R, we were able to execute complex algorithms and analyze large 210 amounts of images with efficiency and precision. Furthermore, the use of specialized 211 libraries and frameworks such as the shiny package, would allow us us to create an 212 interactive web application that effectively presented our findings to a wider audience. 213 Our findings highlight the importance of utilizing cutting-edge technology in research 214 and the potential for significant advancements in the field. As the volume of data 215 continues to grow at an unprecedented rate, the need for efficient and powerful tools to 216 process and analyze this data becomes increasingly important. It is our hope that this 217 study will inspire others to investigate the benefits of using similar tools and techniques 218 in their own research and to continue pushing the boundaries of what is possible with 219 technology. We would like to extend our deepest gratitude to the R community and the developers 230 of the shiny package for providing the powerful tools that made this research possible. 231 The flexibility and capability of the R software allowed us to effectively analyze and 232 visualize our data, while the shiny package made it easy to create an interactive web 233 application for presenting our findings. The support and resources provided by the R 234 community were invaluable in the completion of this project. We are grateful for their 235 contributions to the field of data analysis and visualization.