Computer-aided techniques for chromogenic immunohistochemistry: Status and directions

Immunohistochemistry as a detection tool for ion channels involved in dental pain signaling

Background

Despite advances in pain detection, diagnosis, and management, the prevalence of dental pain is still on the rise. Although dental pain is not directly related to fatal outcomes, the two most common types of dental pain—dental caries and dentin hypersensitivity—have a significant impact on an individual’s quality of life. Understanding the mechanism of the pain pathway is one of the crucial steps in providing better treatment for these patients. Ion channels are critical biomolecules that have been the subject of dental study owing to their roles in the transmission and transduction of external stimuli, as well as in the control and perception of pain. Numerous immunohistochemical (IHC) staining approaches have also been used to identify the many ion channels implicated in peripheral pain signaling in dental pulp.

Highlight

This review highlights the critical steps in IHC and its role in the detection of ion channels involved in the dental pain signaling pathway.

Conclusion

The key ion channels identified using IHC and whose functions have been widely researched in dental tissues are addressed in this review article.

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

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.

RNAscope compatibility with image analysis platforms for the quantification of tissue-based colorectal cancer biomarkers in archival formalin-fixed paraffin-embedded tissue

RNAscope®, has emerged as an important in-situ hybridisation method to validate mRNA expression within single cells whilst preserving tissue morphology in histological samples. The aim of this research was to compare the utility of various open-source and commercial image analysis methods, to quantify mRNA transcripts identified by RNAscope within formalin fixed paraffin embedded (FFPE) histological samples and cell monolayer preparations. Examination of MLH1 expression from 10 histological FFPE colorectal cancer specimens using four image analysis tools (Colour Deconvolution, SpotStudio, WEKA and the LEICA RNA-ISH algorithm) showed the WEKA tool as having the greatest level of agreement with manual quantification. Comparing image analysis methods to qRT-PCR for quantifying MLH1, GFI1 and TNFRSF11A expression within two colorectal cell lines results suggest that these image analysis methods perform at a similar level to qRT-PCR. Furthermore, we describe the strengths and limitations for each image analysis method when used in combination with RNAscope assays. Our study concludes that there are several freely available and commercial image analysis tools that enable reliable RNA in situ expression analysis, however operators need to consider factors, such as expected expression levels of target genes, software usability and functionality.

Androgen and Estrogen Receptor Expression in Different Types of Perianal Gland Tumors in Male Dogs

Perianal gland tumors are modified sebaceous glands present in the skin of the perianal region in the dog. Hormonal stimulation may induce hyperplasia of the perianal glands or their neoplastic progression. The presence of androgen (AR) and estrogen (ER) receptors have been demonstrated both in normal perianal glands as well as in perianal tumors. The aim of the study was an immunohistochemical assessment of the expression of estrogen and androgen receptors in perianal gland tumors in dogs as an applicatory marker for antihormonal treatment. Biopsy samples of perianal masses were collected from 41 male dogs. A histopathological examination revealed 24 adenomas, 12 epitheliomas and five carcinomas. The immunohistochemical staining showed a mainly nuclear expression of AR and ER in the neoplastic cells. Both the androgen and estrogen receptors were expressed in adenoma, epithelioma and carcinoma cases; however, the highest expression of the receptors was stated in the adenoma and epithelioma. In the case of the carcinoma, the expression of sex hormone receptors was very weak. The differences of the number of cells expressing AR and ER as well as the observed differentiated intensity of staining in the studies demonstrated that the determination of the expression of the sex hormone receptors may be useful to elaborate a diagnostic and therapeutic algorithm.

Histo-ELISA technique for quantification and localization of tissue components

A novel Histo-ELISA technique is intended to facilitate quantification of target tissue proteins in a tissue section and involves the selection of target regions in the tissue section, application of streptavidin-conjugated HRP (horseradish peroxidase), coupled with peroxidase substrate—TMB (3,3′,5,5′-tetramethylbenzidine), and staining dye evaluation with ELISA reader. The target protein content (weight per volume unit) was translated from optical densities by a reference standard curve, obtained via parallel staining of the targeted protein-coated slides. To validate the technique, we carried out quantifications of IgG extravasation in ischemic and nonischemic brain sections in a mouse stroke model. With those obtained data and the reference of immunohistochemistry scores assessed on the adjacent sections, accuracy, sensitivity, and precision for the technique were evaluated. For all evaluated parameters, Histo-ELISA performance was either comparable to or better than the standard immunohistochemistry. A comparison with the data from the repeated measurements yielded a rather low coefficient of variation. The results confirmed that the technique is a fairly reliable quantitative test with rather high sensitivity, accuracy, precision, and reproducibility for detecting target protein content in tissue sections and that its tissue distribution and related subsequent morphological changes can be observed at the same time.

Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling

BACKGROUND

The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open.

RESULTS

In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions.

CONCLUSIONS

The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.

Accurate and Unbiased Quantitation of Amyloid-β Fluorescence Images Using ImageSURF

BACKGROUND

Images of amyloid-β pathology characteristic of Alzheimer's disease are difficult to consistently and accurately segment, due to diffuse deposit boundaries and imaging variations.

METHODS

We evaluated the performance of ImageSURF, our open-source ImageJ plugin, which considers a range of image derivatives to train image classifiers. We compared ImageSURF to standard image thresholding to assess its reproducibility, accuracy and generalizability when used on fluorescence images of amyloid pathology.

RESULTS

ImageSURF segments amyloid-β images significantly more faithfully, and with significantly greater generalizability, than optimized thresholding.

CONCLUSION

In addition to its superior performance in capturing human evaluations of pathology images, ImageSURF is able to segment image sets of any size in a consistent and unbiased manner, without requiring additional blinding, and can be retrospectively applied to existing images. The training process yields a classifier file which can be shared as supplemental data, allowing fully open methods and data, and enabling more direct comparisons between different studies.

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections

Background

In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension.

Software

The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers’ relative location). Additionally, it computes novel measures of markers’ co-existence in tissue volumes depending on their density.

Conclusions

Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.

Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images

BACKGROUND AND OBJECTIVE

This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process.

METHODS

The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring.

RESULTS

Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods.

CONCLUSIONS

The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.

Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer

BACKGROUND

Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival.

METHODS

We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app 'Reverse the Odds'. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival.

RESULTS

Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up.

CONCLUSIONS

Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery.

A Clinicopathological Study of Various Oral Cancer Diagnostic Techniques

Oral cancer is one of the most commonly occurring malignant tumors in the head and neck regions with high incident rate and mortality rate in the developed countries than in the developing countries. Generally, the survival rate of cancer patients may increase when diagnosed at early stage, followed by prompt treatment and therapy. Recently, cancer diagnosis and therapy design for a specific cancer patient have been performed with the advanced computer-aided techniques. The responses of the cancer therapy could be continuously monitored to ensure the effectiveness of the treatment process that hardly requires diagnostic result as quick as possible to improve the quality and patient care. This paper gives an overview of oral cancer occurrence, different types, and various diagnostic techniques. In addition, a brief introduction is given to various stages of immunoanalysis including tissue image preparation, whole slide imaging, and microscopic image analysis.

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection

BACKGROUND AND OBJECTIVE

For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step.

METHODS

To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells.

RESULTS

The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches.

CONCLUSIONS

The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other segmentation approaches used for cancer detection.

Objective Quantification of Immune Cell Infiltrates and Epidermal Proliferation in Psoriatic Skin: A Comparison of Digital Image Analysis and Manual Counting

Digital pathology and image analysis have developed extensively during the last couple of years. Especially the advance in whole-slide scanning, software, and computer processing makes it possible to apply these methods in tissue-based research. Today this task is dominated by tedious manual assessments by pathologists with the interobserver and intraobserver variation this includes. Automated quantitative assessment of immunohistochemical staining has the potential to objectively extract numerical measures from cell and tissue structures, and allows efficient high throughput analysis in clinical research. Published data of manual cell counts in psoriatic skin samples were in this study reevaluated using the digital image analysis (DIA) software. Whole slides immunohistochemically stained for CD3, CD4, CD8, CD45R0, and Ki-67 were scanned and quantitatively evaluated using simple threshold analysis. Regression analysis with R values in the range of 0.85 to 0.95 indicates a good correlation between the manual count of cell numbers and the staining density obtained by automated DIA. Moreover, we show that the automated image analysis is reliable over a broad range of thresholds and that it is robust to differences in staining intensities and hence useful for high throughput analysis. DIA is a viable technical approach for automated cell quantification. Its output highly correlates to the conventional manual cell counting and hence allows for increasing the throughput and reducing the analysis time significantly.

Comparison of immunohistochemistry with PCR for assessment of ER, PR, and Ki-67 and prediction of pathological complete response in breast cancer

Background

Proliferation may predict response to neoadjuvant therapy of breast cancer and is commonly assessed by manual scoring of slides stained by immunohistochemistry (IHC) for Ki-67 similar to ER and PgR. This method carries significant intra- and inter-observer variability. Automatic scoring of Ki-67 with digital image analysis (qIHC) or assessment of MKI67 gene expression with RT-qPCR may improve diagnostic accuracy.

Methods

Ki-67 IHC visual assessment was compared to the IHC nuclear tool (AperioTM) on core biopsies from a randomized neoadjuvant clinical trial. Expression of ESR1, PGR and MKI67 by RT-qPCR was performed on RNA extracted from the same formalin-fixed paraffin-embedded tissue. Concordance between the three methods (vIHC, qIHC and RT-qPCR) was assessed for all 3 markers. The potential of Ki-67 IHC and RT-qPCR to predict pathological complete response (pCR) was evaluated using ROC analysis and non-parametric Mann-Whitney Test.

Results

Correlation between methods (qIHC versus RT-qPCR) was high for ER and PgR (spearman´s r = 0.82, p < 0.0001 and r = 0.86, p < 0.0001, respectively) resulting in high levels of concordance using predefined cut-offs. When comparing qIHC of ER and PgR with RT-qPCR of ESR1 and PGR the overall agreement was 96.6 and 91.4%, respectively, while overall agreement of visual IHC with RT-qPCR was slightly lower for ER/ESR1 and PR/PGR (91.2 and 92.9%, respectively). In contrast, only a moderate correlation was observed between qIHC and RT-qPCR continuous data for Ki-67/MKI67 (Spearman’s r = 0.50, p = 0.0001). Up to now no predictive cut-off for Ki-67 assessment by IHC has been established to predict response to neoadjuvant chemotherapy. Setting the desired sensitivity at 100%, specificity for the prediction of pCR (ypT0ypN0) was significantly higher for mRNA than for protein (68.9% vs. 22.2%). Moreover, the proliferation levels in patients achieving a pCR versus not differed significantly using MKI67 RNA expression (Mann-Whitney p = 0.002), but not with qIHC of Ki-67 (Mann-Whitney p = 0.097) or vIHC of Ki-67 (p = 0.131).

Conclusion

Digital image analysis can successfully be implemented for assessing ER, PR and Ki-67. IHC for ER and PR reveals high concordance with RT-qPCR. However, RT-qPCR displays a broader dynamic range and higher sensitivity than IHC. Moreover, correlation between Ki-67 qIHC and RT-qPCR is only moderate and RT-qPCR with MammaTyper® outperforms qIHC in predicting pCR. Both methods yield improvements to error-prone manual scoring of Ki-67. However, RT-qPCR was significantly more specific.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3111-1) contains supplementary material, which is available to authorized users.

A novel quantitative immunohistochemistry method for precise protein measurements directly in formalin-fixed, paraffin-embedded specimens: analytical performance measuring HER2

In clinical routine pathology today, detection of protein in intact formalin-fixed, paraffin-embedded tissue is limited to immunohistochemistry, which is semi-quantitative. This study presents a new and reliable quantitative immunohistochemistry method, qIHC, based on a novel amplification system that enables quantification of protein directly in formalin-fixed, paraffin-embedded tissue by counting of dots. The qIHC technology can be combined with standard immunohistochemistry, and assessed using standard bright-field microscopy or image analysis. The objective was to study analytical performance of the qIHC method. qIHC was tested under requirements for an analytical quantitative test, and compared with ELISA and flow cytometry for quantitative protein measurements. Human epidermal growth factor receptor 2 (HER2) protein expression was measured in five different cell lines with HER2 expression from undetectable with immunohistochemistry to strong positive staining (IHC 3+). Repeatability, reproducibility, robustness, linearity, dynamic range, sensitivity, and quantification limits were evaluated. Reproducibility and robustness were assessed in a setup to resemble daily work in a laboratory using a commercial immunohistochemistry platform. In addition, qIHC was correlated to standard HER2 immunohistochemistry in 44 breast cancer specimens. For all evaluated parameters, qIHC performance was either comparable or better than the reference methods. Furthermore, qIHC has a lower limit of detection than both immunohistochemistry and the ELISA reference method, and demonstrated ability to measure HER2 accurately and precise within a large dynamic range. In conclusion, the results show that qIHC provides a sensitive, quantitative, accurate, and robust assay for measurement of protein expression in formalin-fixed, paraffin-embedded cell lines, and tissue.

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis

Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node–negative and –positive patients (P < .05), but RGB images were not in lymph node–positive patients (P = .101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR = 2.454; 95% confidence interval [CI], 1.636-3.681 vs HR = 2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC = 0.649; 95% CI, 0.577-0.722 vs AUC = 0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images.

Comprehensive Immunohistochemistry: Digital, Analytical and Integrated

Immunohistochemistry (IHC) is widely used in contemporary pathology as a diagnostic and, increasingly, as a prognostic and predictive tool. The main value of the method today comes from a sensitive and specific detection of a protein of interest in the context of tissue architecture and cell populations. One of the major limitations of conventional IHC is related to the fact that the results are usually obtained by visual qualitative or semiquantitative evaluation. While this is sufficient for diagnostic purposes, measurement of prognostic and predictive biomarkers requires better accuracy and reproducibility. Also, objective evaluation of the spatial heterogeneity of biomarker expression as well as the development of combined/integrated biomarkers are in great demand. On the other end of the scale, the rapid development of tissue proteomics accounting for 2D spatial aspects has led to a disruptive concept of next-generation IHC, promising high multiplexing and broad dynamic range quantitative/spatial data on tissue protein expression. This 'evolutionary gap' between conventional and next-generation IHC can be filled by comprehensive IHC based on digital technologies (empowered by quantification and spatial and multiparametric analytics) and integrated into the pathology workflow and information systems. In this paper, we share our perspectives on a comprehensive IHC road map as a multistep development process.

Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections

Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.

Quantitative Time-Resolved Fluorescence Imaging of Androgen Receptor and Prostate-Specific Antigen in Prostate Tissue Sections

Androgen receptor (AR) and prostate-specific antigen (PSA) are expressed in the prostate and are involved in prostate cancer (PCa). The aim of this study was to develop reliable protocols for reproducible quantification of AR and PSA in benign and malignant prostate tissue using time-resolved fluorescence (TRF) imaging techniques. AR and PSA were detected with TRF in tissue microarrays from 91 PCa patients. p63/ alpha-methylacyl-CoA racemase (AMACR) staining on consecutive sections was used to categorize tissue areas as benign or cancerous. Automated image analysis was used to quantify staining intensity. AR intensity was significantly higher in AMACR+ and lower in AMACR- cancer areas as compared with benign epithelium. The PSA intensity was significantly lower in cancer areas, particularly in AMACR- glands. The AR/PSA ratio varied significantly in the AMACR+ tumor cells as compared with benign glands. There was a trend of more rapid disease progression in patients with higher AR/PSA ratios in the AMACR- areas. This study demonstrates the feasibility of developing reproducible protocols for TRF imaging and automated image analysis to study the expression of AR and PSA in benign and malignant prostate. It also highlighted the differences in AR and PSA protein expression within AMACR- and AMACR+ cancer regions.

Application of multispectral imaging in quantitative immunohistochemistry study of breast cancer: a comparative study

Multispectral imaging (MSI) based on imaging and spectroscopy, as relatively novel to the field of histopathology, has been used in biomedical multidisciplinary researches. We analyzed and compared the utility of multispectral (MS) versus conventional red-green-blue (RGB) images for immunohistochemistry (IHC) staining to explore the advantages of MSI in clinical-pathological diagnosis. The MS images acquired of IHC-stained membranous marker human epidermal growth factor receptor 2 (HER2), cytoplasmic marker cytokeratin5/6 (CK5/6), and nuclear marker estrogen receptor (ER) have higher resolution, stronger contrast, and more accurate segmentation than the RGB images. The total signal optical density (OD) values for each biomarker were higher in MS images than in RGB images (all P < 0.05). Moreover, receiver operator characteristic (ROC) analysis revealed that a greater area under the curve (AUC), higher sensitivity, and specificity in evaluation of HER2 gene were achieved by MS images (AUC = 0.91, 89.1 %, 83.2 %) than RGB images (AUC = 0.87, 84.5, and 81.8 %). There was no significant difference between quantitative results of RGB images and clinico-pathological characteristics (P > 0.05). However, by quantifying MS images, the total signal OD values of HER2 positive expression were correlated with lymph node status and histological grades (P = 0.02 and 0.04). Additionally, the consistency test results indicated the inter-observer agreement was more robust in MS images for HER2 (inter-class correlation coefficient (ICC) = 0.95, r s = 0.94), CK5/6 (ICC = 0.90, r s = 0.88), and ER (ICC = 0.94, r s = 0.94) (all P < 0.001) than that in RGB images for HER2 (ICC = 0.91, r s = 0.89), CK5/6 (ICC = 0.85, r s = 0.84), and ER (ICC = 0.90, r s = 0.89) (all P < 0.001). Our results suggest that the application of MS images in quantitative IHC analysis could obtain higher accuracy, reliability, and more information of protein expression in relation to clinico-pathological characteristics versus conventional RGB images. It may become an optimal IHC digital imaging system used in quantitative pathology.

TissueCypher(™): A systems biology approach to anatomic pathology

Background:

Current histologic methods for diagnosis are limited by intra- and inter-observer variability. Immunohistochemistry (IHC) methods are frequently used to assess biomarkers to aid diagnoses, however, IHC staining is variable and nonlinear and the manual interpretation is subjective. Furthermore, the biomarkers assessed clinically are typically biomarkers of epithelial cell processes. Tumors and premalignant tissues are not composed only of epithelial cells but are interacting systems of multiple cell types, including various stromal cell types that are involved in cancer development. The complex network of the tissue system highlights the need for a systems biology approach to anatomic pathology, in which quantification of system processes is combined with informatics tools to produce actionable scores to aid clinical decision-making.

Aims:

Here, we describe a quantitative, multiplexed biomarker imaging approach termed TissueCypher™ that applies systems biology to anatomic pathology. Applications of TissueCypher™ in understanding the tissue system of Barrett's esophagus (BE) and the potential use as an adjunctive tool in the diagnosis of BE are described.

Patients and Methods:

The TissueCypher™ Image Analysis Platform was used to assess 14 epithelial and stromal biomarkers with known diagnostic significance in BE in a set of BE biopsies with nondysplastic BE with reactive atypia (RA, n = 22) and Barrett's with high-grade dysplasia (HGD, n = 17). Biomarker and morphology features were extracted and evaluated in the confirmed BE HGD cases versus the nondysplastic BE cases with RA.

Results:

Multiple image analysis features derived from epithelial and stromal biomarkers, including immune biomarkers and morphology, showed significant differences between HGD and RA.

Conclusions:

The assessment of epithelial cell abnormalities combined with an assessment of cellular changes in the lamina propria may serve as an adjunct to conventional pathology in the assessment of BE.

A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy: Error Estimation and Spectral Consideration

Colour deconvolution is a method used in diagnostic brightfield microscopy to transform colour images of multiple stained biological samples into images representing the stain concentrations. It is applied by decomposing the absorbance values of stain mixtures into absorbance values of single stains. The method assumes a linear relation between stain concentration and absorbance, which is only valid under monochromatic conditions. Diagnostic applications, in turn, are often performed under polychromatic conditions, for which an accurate deconvolution result cannot be achieved. To show this, we establish a mathematical model to calculate non-monochromatic absorbance values based on imaging equipment typically used in histology and use this simulated data as the ground truth to evaluate the accuracy of colour deconvolution. We show the non-linear characteristics of the absorbance formation and demonstrate how it leads to significant deconvolution errors. In particular, our calculations reveal that polychromatic illumination causes 10-times higher deconvolution errors than sequential monochromatic LED illumination. In conclusion, our model can be used for a quantitative assessment of system components--and also to assess and compare colour deconvolution methods.

Digital separation of diaminobenzidine-stained tissues via an automatic color-filtering for immunohistochemical quantification

The digital separation of diaminobenzidine (DAB)-stained tissues from hematoxylin background is an important pre-processing step to analyze immunostains. In most stain separation methods, specific color channels (for example: RGB, HSI, CMYK) or color deconvolution matrices are used to obtain different tissue contrasts between DAB- and hematoxylin-stained areas. However, these methods could produce incomplete separation or color changes because the color spectra of stains and co-localized stains overlap in histological images. Therefore, we proposed an automatic color-filtering to separate hematoxylin- and DAB-stained tissues. In implantation, the RGB images of DAB-labeled immunostains are first converted to 8-bit BN images by a mathematical translation to produce the largest contrast between brown DAB-stained tissues and blue hematoxylin-stained tissues. The first valley in the histogram revised by nonuniform quantization is set as the cut-off point to obtain a brown filter. DAB-stained tissues are accurately delineated from the background counterstain, resulting in DAB-only-image and De-DAB-image. Subsequently, a blue filter is designed in the CIE-Lab color space to further delineate the hematoxylin-stained tissues from the De-DAB-image. Finally, the average values of the remaining pixels of the De-DAB-image are set as the background color of the DAB-only-image to manage uneven dyeing and provide DAB-stained-image for adaptive immunohistochemistry quantitation. Extensive experimental results demonstrated that the proposed method has significant advantages compared with existing methods in terms of complete stain separation without changing the color in DAB-stained areas.

Breast cancer histopathology image analysis: a review

This paper presents an overview of methods that have been proposed for the analysis of breast cancer histopathology images. This research area has become particularly relevant with the advent of whole slide imaging (WSI) scanners, which can perform cost-effective and high-throughput histopathology slide digitization, and which aim at replacing the optical microscope as the primary tool used by pathologist. Breast cancer is the most prevalent form of cancers among women, and image analysis methods that target this disease have a huge potential to reduce the workload in a typical pathology lab and to improve the quality of the interpretation. This paper is meant as an introduction for nonexperts. It starts with an overview of the tissue preparation, staining and slide digitization processes followed by a discussion of the different image processing techniques and applications, ranging from analysis of tissue staining to computer-aided diagnosis, and prognosis of breast cancer patients.

Immunohistochemical assessment of PTEN in vulvar cancer: best practices for tissue staining, evaluation, and clinical association

BACKGROUND

Pten encodes a well-characterized protein that is important in several cancers due to its tumor suppressor function. Yet, the detection and evaluation of PTEN by immunohistochemistry (IHC) for clinical practice have not been standardized. Thus, in this study, we performed a literature review of protocols for PTEN assessment by IHC and the possible differences in evaluation, based on our experience with vulvar carcinomas. Also, we report some of our most recent findings regarding the clinical impact of PTEN in this type of tumor.

METHODS

In total, 150 FFPE vulvar carcinoma samples in a tissue microarray were examined by IHC with regard to PTEN, PI3K, AKT, and mTOR. All evaluations were performed by slide digitalization and quantification using APERIO ImageScope software. All measurements were converted into HScore values for the statistical analysis.

RESULTS

Sharp and specific PTEN expression was observed in the nuclei and cytoplasmic compartments. Its HScore values ranged from 3.5 to 226, with a median of 92.5. mTOR expression was robust in all cases (mean HScore=248.1). AKT and PI3K had median HScore values of 200.5 and 156.5, respectively. In addition, PTEN expression was associated with higher rates of patient survival.

CONCLUSION

The preanalytical step is the first issue in the immunohistochemical evaluation of PTEN. With regard to the analytical procedure, the antigen retrieval step yielded better stains for protocols with high-pH buffers, and antibody clone 6H2.1 effected the most reliable results. PTEN is a good prognostic marker for vulvar cancer, correlating with higher rates of patient survival. Our data underscore the importance of technical standardization to ensure more reliable and reproducible evaluation of PTEN in clinical practice.

Primordial follicle activation in the ovary of Ames dwarf mice

Background

The insulin receptor substrate 1 (IRS1), phosphoinositide 3-kinase (Pi3k), protein kinase B (Akt1), Forkhead Box O3a (FOXO3a) pathway is directly involved in aging and ovarian activation of follicle growth. Therefore, the aim of this work was to measure the expression of genes related to the ovarian pathway for activation of primordial follicles and FOXO3a protein phosphorylation between young and old female Ames dwarf (df/df) and normal (N) mice.

Methods

For this study ovaries from N (n = 10) and df/df (n = 10) female mice were collected at 5–6 months of age and at 21–22 months of age. For immunohistochemistry ovaries from 12 month-old and df/df mice were used.

Results

The expression of Irs1, Pi3k, Akt1, mammalian target of rapamycin (Mtor), suppressor of cytokine signaling −2 (Socs2), Socs3 was lower (P < 0.05) in older than younger N mice and not different (P > 0.05) between young and old df/df mice. The expression of Foxo3a was also lower (P < 0.05) in old than younger N and df/df mice and was higher (P < 0.05) in old df/df than N mice. Expression of Amh was lower (P < 0.05) in old than young N and df/df mice and was higher (P = 0.0009) in df/df than N mice. Imunnostaining for p-FOXO3 was lower in df/df than N mice (P < 0.001), although FOXO3 immunostaining was not different (P > 0.05) between df/df and N mice.

Conclusions

In sum, the present study indicates that lower expression of Irs1, Socs2, Socs3, Akt1, Pi3k, Mtor and Foxo3a mRNA in the ovaries of older mice of both genotypes is associated to a reduced ovarian activity revealed by lower expression of Amh mRNA. At the same time, ovaries of old df/df mice maintained higher expression of Foxo3a mRNA, which was associated to higher ovarian activity. We have shown that df/df females have a lower level of p-FOXO3 in oocytes from primordial/primary follicles, an important activator of follicular growth. Therefore, this study strongly indicates that Prop1^df mutation causes delayed ovarian aging.

Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value

Background

The expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis.

Methods

Tissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied.

Results

There were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve <0.5 in ROC analysis were combined into integrated parameter 1 and integrated parameter 2, respectively. Multivariate analysis showed that integrated parameter 1 (P = 0.040) was independent prognostic factor of patients' 5-year disease free survival. The hazard risk ratio of integrated parameter 1 was 1.454 (HR 95% CI [1.017–2.078]), higher than that of N stage (HR 1.396, 95% CI [1.125–1.733]) and hormone receptor status (HR 0.575, 95% CI [0.353–0.936]), but lower than that of histological grading (HR 3.370, 95% CI [1.125–5.364]) and T stage (HR 1.610, 95% CI [1.026 –2.527]).

Conclusions

This study indicated integrated parameter 1 of mathematical features (number, circularity and total perimeter) of tumor nests could be a useful parameter to predict the prognosis of early stage breast invasive ductal carcinoma.

Digital image analysis of inflammation markers in colorectal mucosa by using a spatial visualization method

The aim of this study was to apply the spatial visualization method of digital images to quantitative analysis of pro-inflammatory cytokines IL-1, IL-6 and TNF-α in various segments of large bowel excised because of colitis ulcerosa in relation with selected clinical symptoms. Our preliminary study included 17 patients having undergone restorative proctocolectomy. Immunohistochemistry was performed for IL-1, IL-6 and TNF-α. The area fraction and intensity fraction of the cytokines studied were determined by digital image analysis. The results were then categorized using Alfred Immunohistochemistry Score. The expression of IL-1, IL-6 and TNF-α was significantly higher in the rectum than in colonic segments (p<0.01), and was associated with the patients' clinical condition. The method of quantitative immunohistochemistry presented here allows for searching associations between the expression of biomarkers and clinical symptoms. Evaluation of inflammatory cytokines could be recommended in the active stage of the disease with present symptoms of bloody and mucus stools. A higher expression of IL-1, IL-6 and TNF in samples beyond large intestine correlates with an intensified clinical course of the disease. In patients without bleeding and mucus symptoms present in stools, no significant correlations were found. Therefore, the assessment of cytokines during remission or clinically silent stage might not be useful.