Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer

Immunohistochemical Study of Ezrin Expression in Colorectal Carcinoma: A Comparative Study between Objective Method and Digital Quantitative Assessment

BACKGROUND

Colorectal cancer is one of the leading causes of cancer death in both developed and developing nations. It is the third most common type of cancer and the fourth leading cause of cancer-related deaths worldwide. Ezrin is involved in maintaining cell structure and cell motility. Expression levels of the ezrin gene correlate with numerous human malignancies.

MATERIAL AND METHODS

Ezrin expression was evaluated in fifty one cases of colorectal carcinoma by using two methods; objective and quantitative method to determine the statistical relation between ezrin objective analysis score and clinicopathological parameters and to do a comparative study between both methods of analysis.

RESULTS

Ezrin was expressed in 92.2% of cases, and it showed a statistical significant relation with tumor grade. A statistically significant relation was found between ezrin objective analysis score and ezrin quantitative analysis score (P-value <0.05). A strong positive Pearson correlation exists between both methods of analysis (R=0.868).

CONCLUSION

Ezrin has a role in colorectal cancer progression and it might provide clinically valuable information in predicting the behavior of colorectal cancer. Digital pathology offers the potential for improvements in quality, efficacy and safety. It will be necessary to carry out similar studies on a larger sample size in order to elucidate the possible prognostic significance of ezrin in colorectal carcinoma and ensure the ability of digital pathology to transform the practice of diagnostic pathology. 
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AutoIHC-scoring: a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue

In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time-consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making. In view of this, our study aims at developing an automated IHC profiler for quantitative assessment of ER and PR molecular expression from stained tissue images. We propose here to use CMYK colour space for positively and negatively stained cell extraction for proportion score. Also colour features are used for quantitative assessment of intensity scoring among the positively stained cells. Five different machine learning models namely artificial neural network, Naïve Bayes, K-nearest neighbours, decision tree and random forest are considered for learning the colour features using average red, green and blue pixel values of positively stained cell patches. Fifty cases of ER- and PR-stained tissues have been evaluated for validation with the expert pathologist's score. All five models perform adequately where random forest shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.9192). In the proposed approach the average variation of diaminobenzidine (DAB) to nuclear area from the expert's score is found to be 7.58%, as compared to 27.83% for state-of-the-art ImmunoRatio software.

Computer-based image analysis in breast pathology

Whole slide imaging (WSI) has the potential to be utilized in telepathology, teleconsultation, quality assurance, clinical education, and digital image analysis to aid pathologists. In this paper, the potential added benefits of computer-assisted image analysis in breast pathology are reviewed and discussed. One of the major advantages of WSI systems is the possibility of doing computer-based image analysis on the digital slides. The purpose of computer-assisted analysis of breast virtual slides can be (i) segmentation of desired regions or objects such as diagnostically relevant areas, epithelial nuclei, lymphocyte cells, tubules, and mitotic figures, (ii) classification of breast slides based on breast cancer (BCa) grades, the invasive potential of tumors, or cancer subtypes, (iii) prognosis of BCa, or (iv) immunohistochemical quantification. While encouraging results have been achieved in this area, further progress is still required to make computer-based image analysis of breast virtual slides acceptable for clinical practice.

Quantitative comparison and reproducibility of pathologist scoring and digital image analysis of estrogen receptor β2 immunohistochemistry in prostate cancer

Background

Digital image analysis offers advantages over traditional pathologist visual scoring of immunohistochemistry, although few studies examining the correlation and reproducibility of these methods have been performed in prostate cancer. We evaluated the correlation between digital image analysis (continuous variable data) and pathologist visual scoring (quasi-continuous variable data), reproducibility of each method, and association of digital image analysis methods with outcomes using prostate cancer tissue microarrays (TMAs) stained for estrogen receptor-β2 (ERβ2).

Methods

Prostate cancer TMAs were digitized and evaluated by pathologist visual scoring versus digital image analysis for ERβ2 staining within tumor epithelium. Two independent analysis runs were performed to evaluate reproducibility. Image analysis data were evaluated for associations with recurrence-free survival and disease specific survival following radical prostatectomy.

Results

We observed weak/moderate Spearman correlation between digital image analysis and pathologist visual scores of tumor nuclei (Analysis Run A: 0.42, Analysis Run B: 0.41), and moderate/strong correlation between digital image analysis and pathologist visual scores of tumor cytoplasm (Analysis Run A: 0.70, Analysis Run B: 0.69). For the reproducibility analysis, there was high Spearman correlation between pathologist visual scores generated for individual TMA spots across Analysis Runs A and B (Nuclei: 0.84, Cytoplasm: 0.83), and very high correlation between digital image analysis for individual TMA spots across Analysis Runs A and B (Nuclei: 0.99, Cytoplasm: 0.99). Further, ERβ2 staining was significantly associated with increased risk of prostate cancer-specific mortality (PCSM) when quantified by cytoplasmic digital image analysis (HR 2.16, 95 % CI 1.02–4.57, p = 0.045), nuclear image analysis (HR 2.67, 95 % CI 1.20–5.96, p = 0.016), and total malignant epithelial area analysis (HR 5.10, 95 % CI 1.70–15.34, p = 0.004). After adjusting for clinicopathologic factors, only total malignant epithelial area ERβ2 staining was significantly associated with PCSM (HR 4.08, 95 % CI 1.37–12.15, p = 0.012).

Conclusions

Digital methods of immunohistochemical quantification are more reproducible than pathologist visual scoring in prostate cancer, suggesting that digital methods are preferable and especially warranted for studies involving large sample sizes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13000-016-0511-5) contains supplementary material, which is available to authorized users.

Standardized Ki67 Diagnostics Using Automated Scoring--Clinical Validation in the GeparTrio Breast Cancer Study

PURPOSE

Scoring proliferation through Ki67 immunohistochemistry is an important component in predicting therapy response to chemotherapy in patients with breast cancer. However, recent studies have cast doubt on the reliability of "visual" Ki67 scoring in the multicenter setting, particularly in the lower, yet clinically important, proliferation range. Therefore, an accurate and standardized Ki67 scoring is pivotal both in routine diagnostics and larger multicenter studies.

EXPERIMENTAL DESIGN

We validated a novel fully automated Ki67 scoring approach that relies on only minimal a priori knowledge on cell properties and requires no training data for calibration. We applied our approach to 1,082 breast cancer samples from the neoadjuvant GeparTrio trial and compared the performance of automated and manual Ki67 scoring.

RESULTS

The three groups of autoKi67 as defined by low (≤ 15%), medium (15.1%-35%), and high (>35%) automated scores showed pCR rates of 5.8%, 16.9%, and 29.5%, respectively. AutoKi67 was significantly linked to prognosis with overall and progression-free survival P values P(OS) < 0.0001 and P(PFS) < 0.0002, compared with P(OS) < 0.0005 and P(PFS) < 0.0001 for manual Ki67 scoring. Moreover, automated Ki67 scoring was an independent prognosticator in the multivariate analysis with P(OS) = 0.002, P(PFS) = 0.009 (autoKi67) versus P(OS) = 0.007, PPFS = 0.004 (manual Ki67).

CONCLUSIONS

The computer-assisted Ki67 scoring approach presented here offers a standardized means of tumor cell proliferation assessment in breast cancer that correlated with clinical endpoints and is deployable in routine diagnostics. It may thus help to solve recently reported reliability concerns in Ki67 diagnostics.

TCL1 expression predicts overall survival in patients with mantle cell lymphoma

OBJECTIVES

Mantle cell lymphoma (MCL) has a heterogeneous clinical course. Although most cases show a poor prognosis, a minority has an indolent course. It is difficult to identify indolent MCL cases prospectively. T-cell leukemia/lymphoma protein 1 (TCL1) is expressed by several B-cell lymphomas, including MCL. This study examined the expression of TCL1 and its prognostic relevance for MCL.

METHODS

Clinical data for 162 patients with MCL were collected. Of these, 144 cases with available tissues for tissue microarray construction and immunostaining were included in the analysis. TCL1 staining was quantified using the Nuclear Quant application with Pannoramic™ Viewer v. 1.14. High TCL1 expression was defined as moderate to strong nuclear and/or cytoplasmic staining in 40% or more of the cells.

RESULTS

High TCL1 expression was observed in 39 of 144 samples (27.1%). Patients with low TCL1 expression were more likely to present with blastoid/pleomorphic morphology (P = 0.010). Low TCL1 expression was associated with significantly shorter overall survival (OS, P = 0.006). Multivariate analysis identified low TCL1 expression (P = 0.003), high-risk MIPI (P = 0.027), and anemia (P = 0.018) as adverse prognostic factors.

CONCLUSIONS

Our study suggests that TCL1 expression profile may have a role in the prediction of overall outcome in patient with MCL and call for prospective studies.

High Expression of Major Histocompatibility Complex Class I in Clear Cell Renal Cell Carcinoma Is Associated with Improved Prognosis

INTRODUCTION

In this study we analyzed major histocompatibility complex class I (MHCI) expression as a potential prognostic immune marker for patients with clear cell renal cell carcinoma (ccRCC).

PATIENTS AND METHODS

34 patients with localized ccRCC (pT1-pT3) who had undergone nephrectomy and had at least 4 years of clinical follow-up data were included in the study. Immunohistochemical staining for MHCI was performed on tumor sections. An automated image analysis algorithm was applied to representative tumor areas to quantitate the proportion of stained pixels (positivity score = positive pixels/total pixels) on scanned digital slides.

RESULTS

At the end of the study, the patients who were alive had increased MHCI expression (mean positivity score 0.80) compared to those who died of the disease (mean positivity score 0.53; p < 0.0001, t test). Patients who were alive with recurrence had increased MHCI expression (positivity score 0.81) compared to those who succumbed to their disease recurrence (positivity score 0.53; p < 0.0001, t test). Survival was higher among patients with high MHCI expression compared to patients with low MHCI expression (p < 0.0001, Mantel-Cox).

CONCLUSIONS

With an automated high-throughput image analysis technique, this study shows that higher tumor cell MHCI expression promotes increased survival and reduced incidence of recurrence compared to patients with lower tumor cell MHCI expression.

Exploring the spatial dimension of estrogen and progesterone signaling: detection of nuclear labeling in lobular epithelial cells in normal mammary glands adjacent to breast cancer

BACKGROUND

Comprehensive spatial assessment of hormone receptor immunohistochemistry staining in digital whole slide images of breast cancer requires accurate detection of positive nuclei within biologically relevant regions of interest. Herein, we propose a combination of automated region labeling at low resolution and subsequent detailed tissue evaluation of subcellular structures in lobular structures adjacent to breast cancer, as a proof of concept for the approach to analyze estrogen and progesterone receptor expression in the spatial context of surrounding tissue.

METHODS

Routinely processed paraffin sections of hormone receptor-negative ductal invasive breast cancer were stained for estrogen and progesterone receptor by immunohistochemistry. Digital whole slides were analyzed using commercially available image analysis software for advanced object-based analysis, applying textural, relational, and geometrical features. Mammary gland lobules were targeted as regions of interest for analysis at subcellular level in relation to their distance from coherent tumor as neighboring relevant tissue compartment. Lobule detection quality was evaluated visually by a pathologist.

RESULTS

After rule set optimization in an estrogen receptor-stained training set, independent test sets (progesterone and estrogen receptor) showed acceptable detection quality in 33% of cases. Presence of disrupted lobular structures, either by brisk inflammatory infiltrate, or diffuse tumor infiltration, was common in cases with lower detection accuracy. Hormone receptor detection tended towards higher percentage of positively stained nuclei in lobules distant from the tumor border as compared to areas adjacent to the tumor. After adaptations of image analysis, corresponding evaluations were also feasible in hormone receptor positive breast cancer, with some limitations of automated separation of mammary epithelial cells from hormone receptor-positive tumor cells.

CONCLUSIONS

As a proof of concept for object-oriented detection of steroid hormone receptors in their spatial context, we show that lobular structures can be classified based on texture-based image features, unless brisk inflammatory infiltration disrupts the normal morphological structure of the tubular gland epithelium. We consider this approach as prototypic for detection and spatial analysis of nuclear markers in defined regions of interest. We conclude that advanced image analysis at this level of complexity requires adaptation to the individual tumor phenotypes and morphological characteristics of the tumor environment.

Oestrogen receptors β1 and βcx have divergent roles in breast cancer survival and lymph node metastasis

Background:

The expression of oestrogen receptor (ER) α characterises a subset of breast cancers associated with good response to endocrine therapy. However, the clinical significance of the second ER, ERβ1, and its splice variant ERβcx is still unclear.

Methods:

We here report an assessment of ERα, ERβ1 and ERβcx by immunohistochemistry using quantitative digital image analysis of 340 primary tumours and corresponding sentinel lymph nodes.

Results:

No differences were seen in ER levels in primary tumours vs lymph node metastases. ERβ1 and ERβcx were equally distributed among age groups and tumour histological grades. Loss of ERβ1 in the primary tumour was strongly associated with poor survival. Its prognostic impact was particularly evident in young patients and in high-grade tumours. The worst outcome was seen in the tumours lacking both ERα and ERβ1. ERβcx expression in the primary tumour correlated with a higher risk of lymph node metastasis, and with poor survival when expressed in sentinel node lymphocytes.

Conclusions:

Our study reveals highly significant although antagonising roles of ERβ1 and ERβcx in breast cancer. Consequently, we suggest that the histopathological assessment of ERβ1 is of value as a prognostic and potentially predictive biomarker.

Immunohistochemical analysis of medullary breast carcinoma autoantigens in different histological types of breast carcinomas

BACKGROUND

On the past decade a plethora of investigations were directed on identification of molecules involved in breast tumorogenesis, which could represent a powerful tool for monitoring, diagnostics and treatment of this disease. In current study we analyzed six previously identified medullary breast carcinoma autoantigens including LGALS3BP, RAD50, FAM50A, RBPJ, PABPC4, LRRFIP1 with cancer restricted serological profile in different histological types of breast cancer.

METHODS

Semi-quantitative immunohistochemical analysis of 20 tissue samples including medullary breast carcinoma, invasive ductal carcinoma, invasive lobular carcinoma and non-cancerous tissues obtained from patients with fibrocystic disease (each of five) was performed using specifically generated polyclonal antibodies. Differences in expression patterns were evaluated considering percent of positively stained cells, insensitivity of staining and subcellular localization in cells of all tissue samples.

RESULTS

All 6 antigens predominantly expressed in the most cells of all histological types of breast tumors and non-cancerous tissues with slight differences in intensity of staining and subcellular localization. The most significant differences in expression pattern were revealed for RAD50 and LGALS3BP in different histological types of breast cancer and for PABPC4 and FAM50A antigens in immune cells infiltrating breast tumors.

CONCLUSIONS

This pilot study made possible to select 4 antigens LGALS3BP, RAD50, PABPC4, and FAM50A as promising candidates for more comprehensive research as potential molecular markers for breast cancer diagnostics and therapy.

VIRTUAL SLIDES

The virtual slides' for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1860649350796892.

Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring

Abstract

Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1649068103671302

Digital imaging in pathology--current applications and challenges

Conventional histopathology is rapidly shifting towards digital integration. Will microscopes (and pathologists) soon be obsolete? Or are we dealing with just another image modality that leaves the core of tissue diagnosis intact? This article provides an overview of current digital pathology applications and research with emphasis on whole slide imaging (WSI). Static or interactive digital pathology work stations already can be used for many purposes, e.g. telepathology expert consultations, frozen section diagnosis in remote areas, cytology screening, quality assurance, diagnostic validations for clinical trials, quantitation of hormone receptor or HER2 studies in breast cancer, or three-dimensional visualization of anatomical structures, among others. Changes of workflow in histology laboratories are beginning to enable digital image acquisition and WSI in a routine setting. WSI plays an increasing role in pathology education, glass slide boxes in medical schools are being replaced by digital slide collections; digital slide seminars and virtual microscopy are used for postgraduate and continuing medical education in pathology. Research and efforts to validate WSI systems for diagnostic settings are ongoing.

Comparative evaluation of ERα and ERβ significance in breast cancer: state of the art

Over 30 years of clinical data have unequivocally established estrogen receptor (ER)-α as a critical clinical biomarker and valid therapeutic target to fight breast cancer. However, ERα remains imperfect with respect to both of these activities, mainly because the mechanisms by which estrogens mediate their activity are far more complex than originally anticipated. The cloning of a second estrogen receptor, ERβ, has led to a full re-evaluation of our original view of the action of estrogen in breast tissues. Important challenges remain with respect to the design, selection and normalization of the most appropriate methods for assaying the expression and functionality of both receptors. Solving these challenges remains a priority in order to decide upon specific endocrine therapies and save patients who are still dying from a potentially curable disease.