2020
DOI: 10.1111/jmi.12955
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AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

Abstract: Summary Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2‐stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often res… Show more

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Cited by 12 publications
(4 citation statements)
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“…AI has great advantages in the interpretation of IHC results and can be one of possible solutions to increase the accuracy and consistency in HER2 interpretation in the HER2 low-expressing tumors. Although several AI models have been proposed [22][23][24] , the AI algorithm which we proposed in this study was the rst one to focus on the accurate interpretation of HER2 0 and HER2 1+. In the RS1 of this study, there were no signi cant differences among pathologists, and the accuracy in distinguishing HER2 0 from HER2 1 + tumors was poor, regardless of their levels of experience.…”
Section: Discussionmentioning
confidence: 99%
“…AI has great advantages in the interpretation of IHC results and can be one of possible solutions to increase the accuracy and consistency in HER2 interpretation in the HER2 low-expressing tumors. Although several AI models have been proposed [22][23][24] , the AI algorithm which we proposed in this study was the rst one to focus on the accurate interpretation of HER2 0 and HER2 1+. In the RS1 of this study, there were no signi cant differences among pathologists, and the accuracy in distinguishing HER2 0 from HER2 1 + tumors was poor, regardless of their levels of experience.…”
Section: Discussionmentioning
confidence: 99%
“…Mukundan [ 19 ] has employed uniform local binary pattern (ULBP), characteristic curves, entropy and energy features with logistic regression and SVM classifier to score HER2-stained tissue samples. Tewary et al [ 20 ] have utilised colour space-based membrane extraction followed by an SVM classifier for HER2 scoring. Chang et al [ 21 ] have employed the colour channel to extract the morphology, texture and intensity features, and then they were utilised for training the SVM classifier.…”
Section: Related Workmentioning
confidence: 99%
“…This method, also known as computer-assisted imaging (CAI), uses algorithms to identify a labeled structure, usually by physical differences, such as color, to produce the required measurement outcomes—volume, surface area, length, and staining intensity of the labeled structures. Today, different CAI algorithms are available to analyze images from different tissues and IHC staining techniques for both biomedical research and diagnostic laboratories ( Dixon et al, 2015 , Fassler et al, 2020 , Guirado et al, 2018 , Jawad and Abdullah, 2017 , Lee et al, 2019 , Tewary et al, 2021 ).…”
Section: Immunohistochemistry (Ihc)mentioning
confidence: 99%