A public-domain image processing tool for automated quantification of fluorescence in situ hybridisation signals

Clinical application of the HM-1000 image processing for HER2 fluorescence in situ hybridization signal quantification in breast cancer

BACKGROUND

Accurate quantification of human epidermal growth factor receptor 2 (HER2) gene amplification is important for predicting treatment response and prognosis in patients with breast cancer. Fluorescence in situ hybridization (FISH) is the gold standard for the diagnosis of HER2 status, particularly in cases with equivocal status on immunohistochemistry (IHC) staining, but has some limitations of non-classical amplifications and such cases are diagnosed basing on additional IHC and FISH. This study investigated the clinical utility of a novel super-resolution fluorescence microscopy technique for the better FISH signal visualization and HER2 FISH classification.

METHODS

Fourteen breast cancer tissue samples were retrospectively collected between September 2018 and February 2022, and FISH HER2 signal quantification was evaluated by determining the HER2/chromosome 17 centromere (CEP17) ratio and the number of HER2 signals per nucleus in super- versus conventional-resolution images.

RESULTS

Super-resolution images maintained the same overall HER2 diagnosis from routine, but HER2 FISH amplification changed negative to monosomy in two cases. Two Letrozole non-response relapses coincided to monosomy samples. The median number of HER2 signals per nucleus was 7.5 in super-resolution images and 4.0 in conventional-resolution images in HER2-positive samples and 2.8 and 2.1 signals per nucleus, respectively, in HER2-negative samples.

CONCLUSIONS

Super-resolution images improved signal visualization, including a significant difference in the number of countable HER2 and CEP17 signals in a single nucleus compared with conventional-resolution images. Increased accuracy of signal quantification by super-resolution microscopy may provide clinicians with more detailed information regarding HER2 FISH status that allows to better FISH classification such as HER2-low samples.

Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images

Selecting regions of interest (ROI) is a common step in medical image analysis across all imaging modalities. An ROI is a subset of an image appropriate for the intended analysis and identified manually by experts. In modern pathology, the analysis involves processing multidimensional and high resolution whole slide image (WSI) tiles automatically with an overwhelming quantity of structural and functional information. Despite recent improvements in computing capacity, analyzing such a plethora of data is challenging but vital to accurate analysis. Automatic ROI detection can significantly reduce the number of pixels to be processed, speed the analysis, improve accuracy and reduce dependency on pathologists. In this paper, we present an ROI detection method for WSI and demonstrated it for human epidermal growth factor receptor 2 (HER2) grading for breast cancer patients. Existing HER2 grading relies on manual ROI selection, which is tedious, time-consuming and suffers from inter-observer and intra-observer variability. This study found that the HER2 grade changes with ROI selection. We proposed an ROI detection method using Vision Transformer and investigated the role of image magnification for ROI detection. This method yielded an accuracy of 99% using 20 × WSI and 97% using 10 × WSI for the ROI detection. In the demonstration, the proposed method increased the diagnostic agreement to 99.3% with the clinical scores and reduced the time to 15 seconds for automated HER2 grading.

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images

Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridization (FISH) signals and improve the working efficiency of pathologists. An Aitrox artificial intelligence (AI) model based on deep learning was constructed, and a comparison between the AI model and traditional manual counting was performed. In total, 918 FISH images from 320 consecutive invasive breast cancers were analysed and automatically classified into 5 groups according to the 2018 ASCO/CAP guidelines. The overall classification accuracy was 85.33% (157/184) with a mean average precision of 0.735. In Group 5, the most common group, the consistency was as high as 95.90% (117/122), while the consistency was low in the other groups due to the limited number of cases. The causes of this inconsistency, including clustered HER2 signals, coarse CEP17 signals and some section quality problems, were analysed. The developed AI model is a reliable tool for evaluating HER2 amplification statuses, especially for breast cancer in Group 5; additional cases from multiple centres could further improve the accuracy achieved for other groups.

Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images

Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.

Computer-Aided Scoring Of () Gene Amplification Status In Breast Cancer

Background

Identification of HER2 protein overexpression and/or amplification of the HER2 gene are required to qualify breast cancer patients for HER2 targeted therapies. In situ hybridization (ISH) assays that identify HER2 gene amplification function as a stand-alone test for determination of HER2 status and rely on the manual quantification of the number of HER2 genes and copies of chromosome 17 to determine HER2 amplification.

Methods

To assist pathologists, we have developed the uPath HER2 Dual ISH Image Analysis for Breast (uPath HER2 DISH IA) algorithm, as an adjunctive aid in the determination of HER2 gene status in breast cancer specimens. The objective of this study was to compare uPath HER2 DISH image analysis vs manual read scoring of VENTANA HER2 DISH-stained breast carcinoma specimens with ground truth (GT) gene status as the reference. Three reader pathologists reviewed 220, formalin-fixed, paraffin-embedded (FFPE) breast cancer cases by both manual and uPath HER2 DISH IA methods. Scoring results from manual read (MR) and computer-assisted scores (image analysis, IA) were compared against the GT gene status generated by consensus of a panel of pathologists. The differences in agreement rates of HER2 gene status between manual, computer-assisted, and GT gene status were determined.

Results

The positive percent agreement (PPA) and negative percent agreement (NPA) rates for image analysis (IA) vs GT were 97.2% (95% confidence interval [CI]: 95.0, 99.3) and 94.3% (95% CI: 90.8, 97.3) respectively. Comparison of agreement rates showed that the lower bounds of the 95% CIs for the difference of PPA and NPA for IA vs MR were –0.9% and –6.2%, respectively. Further, inter- and intra-reader agreement rates in the IA method were observed with point estimates of at least 96.7%.

Conclusions

Overall, our data show that the uPath HER2 DISH IA is non-inferior to manual scoring and supports its use as an aid for pathologists in routine diagnosis of breast cancer.

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Image analysis algorithm for HER2 amplification using Bright-field ISH in Breast.

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Automated tumor cell selection and quantitation within pathologist defined ROI.

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The image analysis algorithm is non-inferior to manual scoring.

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Integrated solution to support pathologists in determining HER2 gene status.

Automatic quantification of HER2 gene amplification in invasive breast cancer from chromogenic in situ hybridization whole slide images

Human epidermal growth factor receptor 2 (HER2), a transmembrane tyrosine kinase receptor encoded by the ERBB2 gene on chromosome 17q12, is a predictive and prognostic biomarker in invasive breast cancer (BC). Approximately 20% of BC are HER2-positive as a result of ERBB2 gene amplification and overexpression of the HER2 protein. Quantification of HER2 is performed routinely on all invasive BCs, to assist in clinical decision making for prognosis and treatment for HER2-positive BC patients by manually counting gene signals. We propose an automated system to quantify the HER2 gene status from chromogenic in situ hybridization (CISH) whole slide images (WSI) in invasive BC. The proposed method selects untruncated and nonoverlapped singular nuclei from the cancer regions using color unmixing and machine learning techniques. Then, HER2 and chromosome enumeration probe 17 (CEP17) signals are detected based on the RGB intensity and counted per nucleus. Finally, the HER2-to-CEP17 signal ratio is calculated to determine the HER2 amplification status following the ASCO/CAP 2018 guidelines. The proposed method reduced the labor and time for the quantification. In the experiment, the correlation coefficient between the proposed automatic CISH quantification method and pathologist manual enumeration was 0.98. The p -values larger than 0.05 from the one-sided paired t -test ensured that the proposed method yields statistically indifferent results to the reference method. The method was established on WSI scanned by two different scanners. Through the experiments, the capability of the proposed system has been demonstrated.

Automated density-based counting of FISH amplification signals for HER2 status assessment

BACKGROUND

Automated image analysis can make quantification of FISH signals in histological sections more efficient and reproducible. Current detection-based methods, however, often fail to accurately quantify densely clustered FISH signals.

METHODS

We propose a novel density-based approach to quantifying FISH signals. Instead of detecting individual signals, this approach quantifies FISH signals in terms of the integral over a density map predicted by Deep Learning. We apply the density-based approach to the task of counting and determining ratios of ERBB2 and CEN17 signals and compare it to common detection-based and area-based approaches.

RESULTS

The ratios determined by our approach were strongly correlated with results obtained by manual annotation of individual FISH signals (Pearson's r = 0.907). In addition, they were highly consistent with cutoff-scores determined by a pathologist (balanced concordance = 0.971). The density-based approach generally outperformed the other approaches. Its superiority was particularly evident in the presence of dense signal clusters.

CONCLUSIONS

The presented approach enables accurate and efficient automated quantification of FISH signals. Since signals in clusters can hardly be detected individually even by human observers, the density-based quantification performs better than detection-based approaches.

Automated Image Analysis of HER2 Fluorescence In Situ Hybridization to Refine Definitions of Genetic Heterogeneity in Breast Cancer Tissue

Human epidermal growth factor receptor 2 gene- (HER2-) targeted therapy for breast cancer relies primarily on HER2 overexpression established by immunohistochemistry (IHC) with borderline cases being further tested for amplification by fluorescence in situ hybridization (FISH). Manual interpretation of HER2 FISH is based on a limited number of cells and rather complex definitions of equivocal, polysomic, and genetically heterogeneous (GH) cases. Image analysis (IA) can extract high-capacity data and potentially improve HER2 testing in borderline cases. We investigated statistically derived indicators of HER2 heterogeneity in HER2 FISH data obtained by automated IA of 50 IHC borderline (2+) cases of invasive ductal breast carcinoma. Overall, IA significantly underestimated the conventional HER2, CEP17 counts, and HER2/CEP17 ratio; however, it collected more amplified cells in some cases below the lower limit of GH definition by manual procedure. Indicators for amplification, polysomy, and bimodality were extracted by factor analysis and allowed clustering of the tumors into amplified, nonamplified, and equivocal/polysomy categories. The bimodality indicator provided independent cell diversity characteristics for all clusters. Tumors classified as bimodal only partially coincided with the conventional GH heterogeneity category. We conclude that automated high-capacity nonselective tumor cell assay can generate evidence-based HER2 intratumor heterogeneity indicators to refine GH definitions.

ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.

Fully automated fluorescent in situ hybridization (FISH) staining and digital analysis of HER2 in breast cancer: a validation study

HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH) procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA) and 100 (50 selected IHC 2+ and 50 random IHC scores) full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted) with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94) and 93.8% (κ = 0.88), respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer.

Is it necessary to evaluate nuclei in HER2 FISH evaluation?

A new method that simplifies the evaluation of the traditional HER2 fluorescence in situ hybridization (FISH) evaluation in breast cancer was proposed. HER2 status was evaluated in digital images (DIs) captured from 423 invasive breast cancer stained sections. All centromeric/CEP17 and HER2 gene signals obtained from separated stacked DIs were manually counted on the screen. The global ratios were compared with the traditional FISH evaluation and the immunohistochemical status. The 2 FISH scores were convergent in 96.93% of cases, showing an "almost perfect" agreement with a weighted k of 0.956 (95% confidence interval, 0.928-0.985). The new method evaluates at least 3 times more nuclei than traditional methods and also has an almost perfect agreement with the immunohistochemical scores. The proposed enhanced method substantially improves HER2 FISH assessment in breast cancer biopsy specimens because the evaluation of HER2/CEP17 copy numbers is more representative, easier, and faster than the conventional method.

State-of-the-art FISHing: automated analysis of cytogenetic aberrations in interphase nuclei

Interphase fluorescence in situ hybridization (i-FISH) is a powerful tool for visualizing various molecular targets in non-dividing cells. Manual scoring of i-FISH signals is a labor intensive, time-consuming, and error-prone process liable to subjective interpretation. Automated evaluation of signal patterns provides the opportunity to overcome these difficulties. The first report on automated i-FISH analysis has been published 20 years ago and since then several applications have been introduced in the fields of oncology, and prenatal and fertility screening. In this article, we provide an insight into the automated i-FISH analysis including its course, brief history, clinical applications, and advantages and challenges. The lack of guidelines for describing new automated i-FISH methods hampers the precise comparison of performance of various applications published, thus, we make a proposal for a panel of parameters essential to introduce and standardize new applications and reproduce previously described technologies.

'Genetic heterogeneity' in HER2/neu testing by fluorescence in situ hybridization: a study of 2,522 cases

Amplification for the ERBB2 oncogene encoding the HER2/neu protein (HER2) is of predictive and prognostic importance in breast carcinoma. Fluorescence in situ hybridization (FISH) is a widely accepted method for determining HER2 amplification status. A HER2-amplified tumor is defined as having a ratio of HER2 signals to chromosome 17 centromeric probe signals (HER2/CEP17 ratio) exceeding 2.2. However, the presence of scattered cells demonstrating HER2 amplification is of unclear significance. A 2009 panel guideline defined a tumor with 'genetic heterogeneity' as having at least 5% but fewer than 50% of (non-clustered) tumor nuclei with a ratio >2.2. The study objective was to examine the statistical distribution of breast tumors tested by FISH for HER2 amplification, after implementation of this 2009 guideline. We identified 2522 consecutive breast carcinoma cases (2009-2011) tested for HER2 amplification. All cases were tested by FISH using a standard clinical protocol, adhering to established guidelines. For each case, data on cell counts were retrieved electronically. Each tumor was compared with a theoretical normal distribution by quantile-quantile analysis. Of 2522 FISH tests for HER2, 1900 (75%) were non-amplified, 394 (16%) were amplified, and 228 (9%) were HER2-equivocal. A total of 666 (26%) had 'genetic heterogeneity.' Among these 'genetically heterogeneous' cases, the ratio was non-amplified in 430 (64.5%), amplified in 24 (4%), and equivocal in 212 (31.5%). The amplified subpopulation in 'genetically heterogeneous' tumors was larger if the overall ratio was close to 2.2. However, the percentage of nuclei >2.2 in a 'genetically heterogeneous' tumor was not informative of the underlying tumor-cell distribution. We conclude that the proportion of HER2-amplified nuclei within a tumor does not contribute information independent of the actual HER2/CEP17 ratio. Reassessment of the definition of 'genetic heterogeneity' in HER2 testing is warranted.

Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

Background

Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.

Methods

Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.

Results

Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.

Conclusions

The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995

Development and evaluation of a virtual microscopy application for automated assessment of Ki-67 expression in breast cancer

Background

The aim of the study was to develop a virtual microscopy enabled method for assessment of Ki-67 expression and to study the prognostic value of the automated analysis in a comprehensive series of patients with breast cancer.

Methods

Using a previously reported virtual microscopy platform and an open source image processing tool, ImageJ, a method for assessment of immunohistochemically (IHC) stained area and intensity was created. A tissue microarray (TMA) series of breast cancer specimens from 1931 patients was immunostained for Ki-67, digitized with a whole slide scanner and uploaded to an image web server. The extent of Ki-67 staining in the tumour specimens was assessed both visually and with the image analysis algorithm. The prognostic value of the computer vision assessment of Ki-67 was evaluated by comparison of distant disease-free survival in patients with low, moderate or high expression of the protein.

Results

1648 evaluable image files from 1334 patients were analysed in less than two hours. Visual and automated Ki-67 extent of staining assessments showed a percentage agreement of 87% and weighted kappa value of 0.57. The hazard ratio for distant recurrence for patients with a computer determined moderate Ki-67 extent of staining was 1.77 (95% CI 1.31-2.37) and for high extent 2.34 (95% CI 1.76-3.10), compared to patients with a low extent. In multivariate survival analyses, automated assessment of Ki-67 extent of staining was retained as a significant prognostic factor.

Conclusions

Running high-throughput automated IHC algorithms on a virtual microscopy platform is feasible. Comparison of visual and automated assessments of Ki-67 expression shows moderate agreement. In multivariate survival analysis, the automated assessment of Ki-67 extent of staining is a significant and independent predictor of outcome in breast cancer.

Evidence for oxidative stress in the developing cerebellum of the rat after chronic mild carbon monoxide exposure (0.0025% in air)

BACKGROUND

The present study was designed to test the hypothesis that chronic very mild prenatal carbon monoxide (CO) exposure (25 parts per million) subverts the normal development of the rat cerebellar cortex. Studies at this chronic low CO exposure over the earliest periods of mammalian development have not been performed to date. Pregnant rats were exposed chronically to CO from gestational day E5 to E20. In the postnatal period, rat pups were grouped as follows: Group A: prenatal exposure to CO only; group B: prenatal exposure to CO then exposed to CO from postnatal day 5 (P5) to P20; group C: postnatal exposure only, from P5 to P20, and group D, controls (air without CO). At P20, immunocytochemical analyses of oxidative stress markers, and structural and functional proteins were assessed in the cerebellar cortex of the four groups. Quantitative real time PCR assays were performed for inducible (iNOS), neuronal (nNOS), and endothelial (eNOS) nitric oxide synthases.

RESULTS

Superoxide dismutase-1 (SOD1), SOD2, and hemeoxygenase-1 (HO-1) immunoreactivity increased in cells of the cerebellar cortex of CO-exposed pups. INOS and nitrotyrosine immunoreactivity also increased in blood vessels and Purkinje cells (PCs) of pups from group-A, B and C. By contrast, nNOS immunoreactivity decreased in PCs from group-B. Endothelial NOS immunoreactivity showed no changes in any CO-exposed group. The mRNA levels for iNOS were significantly up-regulated in the cerebellum of rats from group B; however, mRNA levels for nNOS and eNOS remained relatively unchanged in groups A, B and C. Ferritin-H immunoreactivity increased in group-B. Immunocytochemistry for neurofilaments (structural protein), synapsin-1 (functional protein), and glutamic acid decarboxylase (the enzyme responsible for the synthesis of the inhibitory neurotransmitter GABA), were decreased in groups A and B. Immunoreactivity for two calcium binding proteins, parvalbumin and calbindin, remained unchanged. The immunoreactivity of the astrocytic marker GFAP increased after prenatal exposure.

CONCLUSION

We conclude that exogenously supplied CO during the prenatal period promotes oxidative stress as indicated by the up-regulation of SOD-1, SOD-2, HO-1, Ferritin-H, and iNOS with increased nitrotyrosine in the rat cerebella suggesting that deleterious and protective mechanisms were activated. These changes correlate with reductions of proteins important to cerebellar function: pre-synaptic terminals proteins (synapsin-1), proteins for the maintenance of neuronal size, shape and axonal quality (neurofilaments) and protein involved in GABAergic neurotransmission (GAD). Increased GFAP immunoreactivity after prenatal CO-exposure suggests a glial mediated response to the constant presence of CO. There were differential responses to prenatal vs. postnatal CO exposure: Prenatal exposure seems to be more damaging; a feature exemplified by the persistence of markers indicating oxidative stress in pups at P20, following prenatal only CO-exposure. The continuation of this cellular environment up to day 20 after CO exposure suggests the condition is chronic. Postnatal exposure without prenatal exposure shows the least impact, whereas prenatal followed by postnatal exposure exhibits the most pronounced outcome among the groups.

Automated image analysis in histopathology: a valuable tool in medical diagnostics

Virtual pathology, the process of assessing digital images of histological slides, is gaining momentum in today's laboratory environment. Indeed, digital image acquisition systems are becoming commonplace, and associated image analysis solutions are viewed by most as the next critical step in automated histological analysis. Here, we document the advances in the technology, with reference to past and current techniques in histological assessment. In addition, the demand for these technologies is analyzed with major players profiled. As there are several image analysis software programs focusing on the quantification of immunohistochemical staining, particular attention is paid to this application in this review. Oncology has been a primary target area for these approaches, with example studies in this therapeutic area being covered here. Toxicology-based image analysis solutions are also profiled as these are steadily increasing in popularity, especially within the pharmaceutical industry. Reinforced by the phenomenal growth of the virtual pathology field, it is envisioned that the market for automated image analysis tools will greatly expand over the next 10 years.

Automated FISH analysis using dual-fusion and break-apart probes on paraffin-embedded tissue sections

Detecting balanced translocations using tissue sections plays an important diagnostic role in cases of hematological malignancies. Manual scoring is often problematic due to truncation and overlapping of nuclei. Reports have described automated analysis using primarily tile sampling. The aim of this study was to investigate an automated fluorescent in situ hybridization analysis method using grid sampling on tissue sections, and compare the performance of dual-fusion (DF) and break-apart (BA) probes in this setting. Ten follicular, 10 mantle cell lymphoma, and 10 translocation-negative samples were used to set the threshold of false positivity using IGH/CCND1, IGH/BCL-2 DF, and IGH BA probes. The cut-off distances of red and green signals to define fusion signals were 0.5, 1.0, and 1.2 mum for the IGH/CCND1, IGH/BCL-2 DF, and IGH BA probes, respectively. The mean false positivity of grid units was 5.3, 11.4, and 28.1%, respectively. Ten to 14 additional samples analyzed blindly and were correctly classified using each probe. Discriminating positive and negative samples using automated analysis and grid sampling was possible with each probe, although different definitions of fusion signals were required due to the different physical distances between the DNA probes. Using the DF probes resulted in lower false positivity, which was less affected by signal numbers per grid units.