Matrix Metalloproteinase-9 Expression Is Associated with the Absence of Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Patients.

Triple-negative breast cancer (TNBC) is particularly challenging due to the weak or absent response to therapeutics and its poor prognosis. The effectiveness of neoadjuvant chemotherapy (NAC) response is strongly influenced by changes in elements of the tumor microenvironment (TME). This work aimed to characterize the residual TME composition in 96 TNBC patients using immunohistochemistry and in situ hybridization techniques and evaluate its prognostic implications for partial responders vs. non-responders. Compared with non-responders, partial responders containing higher levels of CD83+ mature dendritic cells, FOXP3+ regulatory T cells, and IL-15 expression but lower CD138+ cell concentration exhibited better OS and RFS. However, along with tumor diameter and positive nodal status at diagnosis, matrix metalloproteinase-9 (MMP-9) expression in the residual TME was identified as an independent factor associated with the impaired response to NAC. This study yields new insights into the key components of the residual tumor bed, such as MMP-9, which is strictly associated with the lack of a pathological response to NAC. This knowledge might help early identification of TNBC patients less likely to respond to NAC and allow the establishment of new therapeutic targets.

Prognostic Implications of the Residual Tumor Microenvironment after Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Patients without Pathological Complete Response.

With a high risk of relapse and death, and a poor or absent response to therapeutics, the triple-negative breast cancer (TNBC) subtype is particularly challenging, especially in patients who cannot achieve a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Although the tumor microenvironment (TME) is known to influence disease progression and the effectiveness of therapeutics, its predictive and prognostic potential remains uncertain. This work aimed to define the residual TME profile after NAC of a retrospective cohort with 96 TNBC patients by immunohistochemical staining (cell markers) and chromogenic in situ hybridization (genetic markers). Kaplan-Meier curves were used to estimate the influence of the selected TME markers on five-year overall survival (OS) and relapse-free survival (RFS) probabilities. The risks of each variable being associated with relapse and death were determined through univariate and multivariate Cox analyses. We describe a unique tumor-infiltrating immune profile with high levels of lymphocytes (CD4, FOXP3) and dendritic cells (CD21, CD1a and CD83) that are valuable prognostic factors in post-NAC TNBC patients. Our study also demonstrates the value of considering not only cellular but also genetic TME markers such as MUC-1 and CXCL13 in routine clinical diagnosis to refine prognosis modelling.

CD68 and CD83 immune populations in non-metastatic axillary lymph nodes are of prognostic value for the survival and relapse of breast cancer patients.

BACKGROUND: The foremost cause of death of breast cancer (BC) patients is metastasis, and the first site to which BC predominantly metastasizes is the axillary lymph node (ALN). Thus, ALN status is a key prognostic indicator at diagnosis. The immune system has an essential role in cancer progression and dissemination, so its evaluation in ALNs could have significant applications. In the present study we aimed to investigate the association of clinical-pathological and immune variables in the primary tumour and non-metastatic ALNs (ALNs-) of a cohort of luminal A and triple-negative BC (TNBC) patients with cancer-specific survival (CSS) and time to progression (TTP). METHODS: We analysed the differences in the variables between patients with different outcomes, created univariate and multivariate Cox regression models, validated them by bootstrapping and multiple imputation of missing data techniques, and used Kaplan-Meier survival curves for a 10-years follow-up. RESULTS: We found some clinical-pathological variables at diagnosis (tumour diameter, TNBC molecular profile and presence of ALN metastasis), and the levels of several immune markers in the two studied sites, to be associated with worse CSS and TTP. Nevertheless, only CD68 and CD83 in ALNs- were confirmed as independent prognostic factors for TTP. CONCLUSIONS: The study identified the importance of macrophage and dendritic cell markers as prognostic factors of relapse for BC. We highlight the importance of studying the immune response in ALNs-, which could be relevant to the prediction of BC patients' outcome.

What do we know about inflammatory myofibroblastic tumors? - A systematic review.

BACKGROUND: Inflammatory myofibroblastic tumors (IMTs) are rare intermediate-grade neoplasms that have a high recurrence rate after excision and exhibit low metastatic potential. These tumors contain proliferating neoplastic, fibroblastic and myofibroblastic cells, and are also characterized by chronic inflammatory infiltration by lymphocytes, plasma cells, eosinophils, and histiocytes. They belong to the group of inflammatory spindle cell lesions. Some reactive lesions, such as inflammatory pseudotumors, may appear to be IMTs, which makes their differential diagnosis extremely difficult. The aim of this article is to compile the recent information on IMTs to aid in their diagnosis and treatment. METHODS: We reviewed articles published between 2017 and 2021, which were selected from online medical databases. In addition, some earlier articles and latest scientific monographies were analyzed. RESULTS: The terminology used for inflammatory spindle cell lesions seems to be confusing. The terms "inflammatory myofibroblastic tumors" and "inflammatory pseudotumors" are interchangeably used by many scientists. However, a detailed analysis of the development of terminology suggests that the term "inflammatory myofibroblastic tumors" should be used to refer to a neoplastic lesion. CONCLUSIONS: IMTs are rare neoplasms, which have not been investigated in detail due to the difficulty in collecting a large number of cases. Thus, our knowledge about this disease remains unsatisfactory. Recently developed techniques such as next-generation sequencing and computer-aided histopathological diagnosis may be useful in understanding the etiopathology of IMTs, which will help in the selection of the most appropriate therapy for patients.

How the variability between computer-assisted analysis procedures evaluating immune markers can influence patients' outcome prediction.

Differences between computer-assisted image analysis (CAI) algorithms may cause discrepancies in the identification of immunohistochemically stained immune biomarkers in biopsies of breast cancer patients. These discrepancies have implications for their association with disease outcome. This study aims to compare three CAI procedures (A, B and C) to measure positive marker areas in post-neoadjuvant chemotherapy biopsies of patients with triple-negative breast cancer (TNBC) and to explore the differences in their performance in determining the potential association with relapse in these patients. A total of 3304 digital images of biopsy tissue obtained from 118 TNBC patients were stained for seven immune markers using immunohistochemistry (CD4, CD8, FOXP3, CD21, CD1a, CD83, HLA-DR) and were analyzed with procedures A, B and C. The three methods measure the positive pixel markers in the total tissue areas. The extent of agreement between paired CAI procedures, a principal component analysis (PCA) and Cox multivariate analysis was assessed. Comparisons of paired procedures showed close agreement for most of the immune markers at low concentration. The probability of differences between the paired procedures B/C and B/A was generally higher than those observed in C/A. The principal component analysis, largely based on data from CD8, CD1a and HLA-DR, identified two groups of patients with a significantly lower probability of relapse than the others. The multivariate regression models showed similarities in the factors associated with relapse for procedures A and C, as opposed to those obtained with procedure B. General agreement among the results of CAI procedures would not guarantee that the same predictive breast cancer markers were consistently identified. These results highlight the importance of developing additional strategies to improve the sensitivity of CAI procedures.

System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL).

This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.

Differences in the Immune Response of the Nonmetastatic Axillary Lymph Nodes between Triple-Negative and Luminal A Breast Cancer Surrogate Subtypes.

Breast cancer (BC) comprises four immunohistochemical surrogate subtypes of which triple-negative breast cancer (TNBC) has the highest risk of mortality. Axillary lymph nodes (ALNs) are the regions where BC cells first establish before distant metastasis, and the presence of tumor cells in the ALN causes an immune tolerance profile that contrasts with that of the nonmetastatic ALN (ALN-). However, few studies have compared the immune components of the ALNs- in BC subtypes. The present study aimed to determine whether differences between immune populations in the primary tumor and ALNs- were associated with the luminal A or TNBC subtype. We evaluated a retrospective cohort of 144 patients using paraffin-embedded biopsies. The TNBC samples tended to have a higher histologic grade and proliferation index and had higher levels of immune markers compared with luminal A in primary tumors and ALNs-. Two methods for validating the multivariate analysis found that histologic grade, intratumoral S100 dendritic cells, and CD8 T lymphocytes and CD57 natural killer cells in the ALNs- were factors associated with TNBC, whereas CD83 dendritic cells in the ALNs- were associated with the luminal A subtype. In conclusion, we found that intratumoral regions and ALNs- of TNBC contained higher concentrations of markers related to immune tolerance than luminal A. This finding partially explains the worse prognosis of patients with TNBC.

Peritumoral immune infiltrates in primary tumours are not associated with the presence of axillary lymph node metastasis in breast cancer: a retrospective cohort study.

BACKGROUND: The axillary lymph nodes (ALNs) in breast cancer patients are the body regions to where tumoral cells most often first disseminate. The tumour immune response is important for breast cancer patient outcome, and some studies have evaluated its involvement in ALN metastasis development. Most studies have focused on the intratumoral immune response, but very few have evaluated the peritumoral immune response. The aim of the present article is to evaluate the immune infiltrates of the peritumoral area and their association with the presence of ALN metastases. METHODS: The concentration of 11 immune markers in the peritumoral areas was studied in 149 patients diagnosed with invasive breast carcinoma of no special type (half of whom had ALN metastasis at diagnosis) using tissue microarrays, immunohistochemistry and digital image analysis procedures. The differences in the concentration of the immune response of peritumoral areas between patients diagnosed with and without metastasis in their ALNs were evaluated. A multivariate logistic regression model was developed to identify the clinical-pathological variables and the peritumoral immune markers independently associated with having or not having ALN metastases at diagnosis. RESULTS: No statistically significant differences were found in the concentrations of the 11 immune markers between patients diagnosed with or without ALN metastases. Patients with metastases in their ALNs had a higher histological grade, more lymphovascular and perineural invasion and larger-diameter tumours. The multivariate analysis, after validation by bootstrap simulation, revealed that only tumour diameter (OR = 1.04; 95% CI [1.00-1.07]; p = 0.026), lymphovascular invasion (OR = 25.42; 95% CI [9.57-67.55]; p < 0.001) and histological grades 2 (OR = 3.84; 95% CI [1.11-13.28]; p = 0.033) and 3 (OR = 5.18; 95% CI [1.40-19.17]; p = 0.014) were associated with the presence of ALN metastases at diagnosis. This study is one of the first to study the association of the peritumoral immune response with ALN metastasis. We did not find any association of peritumoral immune infiltrates with the presence of ALN metastasis. Nevertheless, this does not rule out the possibility that other peritumoral immune populations are associated with ALN metastasis. This matter needs to be examined in greater depth, broadening the types of peritumoral immune cells studied, and including new peritumoral areas, such as the germinal centres of the peritumoral tertiary lymphoid structures found in extensively infiltrated neoplastic lesions.

The Immune Response in Nonmetastatic Axillary Lymph Nodes Is Associated with the Presence of Axillary Metastasis and Breast Cancer Patient Outcome.

Tumor cells can modify the immune response in primary tumors and in the axillary lymph nodes with metastasis (ALN+) in breast cancer (BC), influencing patient outcome. We investigated whether patterns of immune cells in the primary tumor and in the axillary lymph nodes without metastasis (ALN-) differed between patients diagnosed without ALN+ (diagnosed-ALN-) and with ALN+ (diagnosed-ALN+) and the implications for clinical outcome. Eleven immune markers were studied using immunohistochemistry, tissue microarray, and digital image analysis in 141 BC patient samples (75 diagnosed-ALN+ and 66 diagnosed-ALN-). Two logistic regression models were derived to identify the clinical, pathologic, and immunologic variables associated with the presence of ALN+ at diagnosis. There are immune patterns in the ALN- associated with the presence of ALN+ at diagnosis. The regression models revealed a small subgroup of diagnosed-ALN+ with ALN- immune patterns that were more similar to those of the ALN- of the diagnosed-ALN-. This small subgroup also showed similar clinical behavior to that of the diagnosed-ALN-. Another small subgroup of diagnosed-ALN- with ALN- immune patterns was found whose members were more similar to those of the ALN- of the diagnosed-ALN+. This small subgroup had similar clinical behavior to the diagnosed-ALN+. These data suggest that the immune response present in ALN- at diagnosis could influence the clinical outcome of BC patients.

Computer analysis of histopathological images for tumor grading. 2.

OBJECTIVE: We have upgraded our own original color filtration pixel-by-pixel (CFPP) method (Klonowski et al 2018a Physiol. Meas. 39 034002) to enable not only automatic and rapid assessment of the proliferation index of a tumor or neoplasm but also quick automatic location of hot-spots (regions of interest, ROIs) in immunohistochemically stained microscopic images of neoplasms and tumors. APPROACH: Neoplastic cells stain differently from normal cells. By counting in a given window the number of pixels belonging to the given subspaces of (R,G,B) color space which correspond, respectively, to proliferating cells (which are mostly neoplastic) and non-proliferating cells (which are mostly normal) we calculate the local proliferation index in this window. The window is moved all around the whole histopathological virtual slide (WSI) or around a chosen part of the WSI. By adding the respective numbers calculated for all the windows covering the WSI or the chosen part of it one can easily calculate the global proliferation index. MAIN RESULTS: The method is rapid and does not require the time-consuming step of selecting ROIs manually nor does it need computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist's way of thinking. We apply our method to a set of slide images of diffuse large B-cell lymphoma. SIGNIFICANCE: By appropriate changes in the (R,G,B) color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology. Because of its rapidity and simplicity it may also used for analysis of series of images to assess local dynamics of image complexity in network physiology applications.

Computer analysis of histopathological images for tumor grading.

OBJECTIVE: We developed a new method that enables automatic and rapid assessment of a tumor's proliferation index from immunohistochemically (IHC) stained microscopic images. APPROACH: The method is based on computer-aided analysis of images - color filtration pixel-by-pixel (CFPP method) of the whole histopathological virtual slides. MAIN RESULTS: The method is simple, rapid, and does not require the time consuming step of selecting manually areas of interest nor the need for computationally complicated detection of hot-spots, both of which attempt to emulate a pathologist's way of estimating a proliferation index. We apply our method to a set of diffuse large B-cell lymphoma (DLBCL) slide images. SIGNIFICANCE: By appropriate changes in the color filtration thresholds, our method may be adapted to the analysis of other types of tumors. It may also be adapted for analysis of microscopic images in neuropathology, like biopsies of dystrophic muscles. Because of its simplicity and rapidity it may also be applied for analysis of series of images to assess dynamics of image complexity in network physiology.

Evaluation of cytokeratin-19 in breast cancer tissue samples: a comparison of automatic and manual evaluations of scanned tissue microarray cylinders.

BACKGROUND: Digital image (DI) analysis avoids visual subjectivity in interpreting immunohistochemical stains and provides more reproducible results. An automated procedure consisting of two variant methods for quantifying the cytokeratin-19 (CK19) marker in breast cancer tissues is presented. METHODS: The first method (A) excludes the holes inside selected CK19 stained areas, and the second (B) includes them. 93 DIs scanned from complete cylinders of tissue microarrays were evaluated visually by two pathologists and by the automated procedures. RESULTS AND CONCLUSIONS: There was good concordance between the two automated methods, both of which tended to identify a smaller CK19-positive area than did the pathologists. The results obtained with method B were more similar to those of the pathologists; probably because it takes into account the entire positive tumoural area, including the holes. However, the pathologists overestimated the positive area of CK19. Further studies are needed to confirm the utility of this automated procedure in prognostic studies.

Comparison of the manual, semiautomatic, and automatic selection and leveling of hot spots in whole slide images for Ki-67 quantification in meningiomas.

Background. This paper presents the study concerning hot-spot selection in the assessment of whole slide images of tissue sections collected from meningioma patients. The samples were immunohistochemically stained to determine the Ki-67/MIB-1 proliferation index used for prognosis and treatment planning. Objective. The observer performance was examined by comparing results of the proposed method of automatic hot-spot selection in whole slide images, results of traditional scoring under a microscope, and results of a pathologist's manual hot-spot selection. Methods. The results of scoring the Ki-67 index using optical scoring under a microscope, software for Ki-67 index quantification based on hot spots selected by two pathologists (resp., once and three times), and the same software but on hot spots selected by proposed automatic methods were compared using Kendall's tau-b statistics. Results. Results show intra- and interobserver agreement. The agreement between Ki-67 scoring with manual and automatic hot-spot selection is high, while agreement between Ki-67 index scoring results in whole slide images and traditional microscopic examination is lower. Conclusions. The agreement observed for the three scoring methods shows that automation of area selection is an effective tool in supporting physicians and in increasing the reliability of Ki-67 scoring in meningioma.

Development of automated quantification methodologies of immunohistochemical markers to determine patterns of immune response in breast cancer: a retrospective cohort study.

INTRODUCTION: Lymph nodes are one of the main sites where an effective immune response develops. Normally, axillary nodes are the first place where breast cancer produces metastases. Several studies have demonstrated the importance of immune cells, especially dendritic cells, in the evolution of breast cancer. The goal of the project is to identify differences in the patterns of immune infiltrates, with particular emphasis on dendritic cells, in tumour and axillary node biopsies between patients with and without metastases in the axillary nodes at the time of diagnosis. It is expected that these differences will be able to explain differences in survival, relapse and clinicopathological variables between the two groups. METHODS AND ANALYSIS: The study will involve 100 patients diagnosed with invasive breast cancer between 2000 and 2007, 50% of whom have metastases in the axillary lymph node at diagnosis. In selected patients, two cylinders from biopsies of representative areas of tumour and axillary nodes (with and without metastasis) will be selected and organised in tissue microarrays. Samples will be stained using immunohistochemical techniques for different markers of immune response and dendritic cells. Two images of each cylinder will be captured under standardised conditions for each marker. Each marker will be quantified automatically by digital image procedures using Image-Pro Plus and Image-J software. Associations of survival, relapse and other clinicopathological variables with the automatically quantified levels of immune infiltrates in patients with and without axillary node metastasis will be sought. ETHICS AND DISSEMINATION: The present project has been approved by the Clinical Research Ethics Committee of the Hospital Universitari Joan XXIII (Ref: 22p/2011). Those patients whose biopsies and clinical data are to be used will give their signed informed consent. Results will be published in peer-reviewed journals.

The influence of the microscope lamp filament colour temperature on the process of digital images of histological slides acquisition standardization.

BACKGROUND: The aim of this study is to compare the digital images of the tissue biopsy captured with optical microscope using bright field technique under various light conditions. The range of colour's variation in immunohistochemically stained with 3,3'-Diaminobenzidine and Haematoxylin tissue samples is immense and coming from various sources. One of them is inadequate setting of camera's white balance to microscope's light colour temperature. Although this type of error can be easily handled during the stage of image acquisition, it can be eliminated with use of colour adjustment algorithms. The examination of the dependence of colour variation from microscope's light temperature and settings of the camera is done as an introductory research to the process of automatic colour standardization. METHODS: Six fields of view with empty space among the tissue samples have been selected for analysis. Each field of view has been acquired 225 times with various microscope light temperature and camera white balance settings. The fourteen randomly chosen images have been corrected and compared, with the reference image, by the following methods: Mean Square Error, Structural SIMilarity and visual assessment of viewer. RESULTS: For two types of backgrounds and two types of objects, the statistical image descriptors: range, median, mean and its standard deviation of chromaticity on a and b channels from CIELab colour space, and luminance L, and local colour variability for objects' specific area have been calculated. The results have been averaged for 6 images acquired in the same light conditions and camera settings for each sample. CONCLUSIONS: The analysis of the results leads to the following conclusions: (1) the images collected with white balance setting adjusted to light colour temperature clusters in certain area of chromatic space, (2) the process of white balance correction for images collected with white balance camera settings not matched to the light temperature moves image descriptors into proper chromatic space but simultaneously the value of luminance changes. So the process of the image unification in a sense of colour fidelity can be solved in separate introductory stage before the automatic image analysis.

Automatic analysis of 2D polyacrylamide gels in the diagnosis of DNA polymorphisms.

INTRODUCTION: The analysis of polyacrylamide gels is currently carried out manually or automatically. In the automatic method, there are limitations related to the acceptable degree of distortion of lane and band continuity. The available software cannot deal satisfactorily with this type of situations. Therefore, the paper presents an original image analysis method devoid of the aforementioned drawbacks. MATERIAL: This paper examines polyacrylamide gel images from Li-Cor DNA Sequencer 4300S resulting from the use of the electrophoretic separation of DNA fragments. The acquired images have a resolution dependent on the length of the analysed DNA fragments and typically it is MG×NG=3806×1027 pixels. The images are saved in TIFF format with a grayscale resolution of 16 bits/pixel. The presented image analysis method was performed on gel images resulting from the analysis of DNA methylome profiling in plants exposed to drought stress, carried out with the MSAP (Methylation Sensitive Amplification Polymorphism) technique. RESULTS: The results of DNA polymorphism analysis were obtained in less than one second for the Intel Core™ 2 Quad CPU Q9300@2.5GHz, 8GB RAM. In comparison with other known methods, specificity was 0.95, sensitivity = 0.94 and AUC (Area Under Curve) = 0.98. CONCLUSIONS: It is possible to carry out this method of DNA polymorphism analysis on distorted images of polyacrylamide gels. The method is fully automatic and does not require any operator intervention. Compared with other methods, it produces the best results and the resulting image is easy to interpret. The presented method of measurement is used in the practical analysis of polyacrylamide gels in the Department of Genetics at the University of Silesia in Katowice, Poland.

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3'-Diaminobenzidine&Haematoxylin.

The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the 'brown component' extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without undercutting the area for true positive objects but with extra false positive objects. The Sauvola and the Bernsen methods gives complementary results what will be exploited when the new method of virtual tissue slides segmentation be develop. VIRTUAL SLIDES: The virtual slides for this article can be found here: slide 1: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617947952577 and slide 2: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617948230017.

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.

Influence of the measurement method of features in ultrasound images of the thyroid in the diagnosis of Hashimoto's disease.

INTRODUCTION: This paper shows the influence of a measurement method of features in the diagnosis of Hashimoto's disease. Sensitivity of the algorithm to changes in the parameters of the ROI, namely shift, resizing and rotation, has been presented. The obtained results were also compared to the methods known from the literature in which decision trees or average gray level thresholding are used. MATERIAL: In the study, 288 images obtained from patients with Hashimoto's disease and 236 images from healthy subjects have been analyzed. For each person, an ultrasound examination of the left and right thyroid lobe in transverse and longitudinal sections has been performed. METHOD: With the use of the developed algorithm, a discriminant analysis has been conducted for the following five options: linear, diaglinear, quadratic, diagquadratic and mahalanobis. The left and right thyroid lobes have been analyzed both together and separately in transverse and longitudinal sections. In addition, the algorithm enabled to analyze specificity and sensitivity as well as the impact of sensitivity of ROI shift, repositioning and rotation on the measured features. RESULTS AND SUMMARY: The analysis has shown that the highest accuracy was obtained for the longitudinal section (LD) with the method of linear, yielding sensitivity = 76%, specificity = 95% and accuracy ACC = 84%. The conducted sensitivity assessment confirms that changes in the position and size of the ROI have little effect on sensitivity and specificity. The analysis of all cases, that is, images of the left and right thyroid lobes in transverse and longitudinal sections, has shown specificity ranging from 60% to 95% and sensitivity from 62% to 89%. Additionally, it was shown that the value of ACC for the method using decision trees as a classifier is equal to 84% for the analyzed data. Thresholding of average brightness of the ROI gave ACC equal to 76%.

Digital image analysis in breast cancer: an example of an automated methodology and the effects of image compression.

In the current practice of pathology, the evaluation of immunohistochemical (IHC) markers represents an essential tool. The manual quantification of these markers is still laborious and subjective, and the use of computerized systems for digital image (DI) analysis has not yet resolved the problems of nuclear aggregates (clusters). Furthermore, the volume of DI storage continues to be an important problem in computer-assisted pathology. In the present study we have developed an automated procedure to quantify IHC nuclear markers in DI with a high level of clusters. Furthermore the effects of JPEG compression in the image analysis were evaluated. The results indicated that there was an agreement with the results of both methods (automated vs. manual) in almost 90% of the analyzed images. On the other hand, automated count differences increase as the compression level increase, but only in images with a high number of stained nuclei (>nuclei/image) or with high area cluster (>25µm2). Some corrector factors were developed in order to correct this count differences. In conclusion, the proposed automated procedure is an objective, faster than manual counting and reproducible method that has more than 90% of similarity with manual count. Moreover, the results demonstrate that with correction factors, it is possible to carry out unbiased automated quantifications on IHC nuclear markers in compressed DIs.