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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.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.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.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.

Simple Summary Triple-negative breast cancer (TNBC) is currently in the clinical research spotlight because of the tumor's aggressive and invasive nature and the scarcity of therapeutic targets. Despite recent advances in identifying reliable prognostic biomarkers in the tumor microenvironment (TME), rigorous evaluation of their predictive capacity remains challenging. We describe the immune cellular and genetic profile of the residual tumor of TNBC that does not achieve a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). A high concentration of lymphocytes and dendritic cells, as well as genetic TME markers such as MUC-1 and CXCL13 in the residual tumor, are valuable prognostic factors of survival and relapse in TNBC patients. From a clinical health perspective, a thorough understanding of the composition of the TME and its prognostic implications might yield relevant immunological information and reveal key predictive biomarkers. This could ultimately help substantially improve the outcomes of residual cancer-burdened TNBC patients after NAC. 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.

With the advent and great advances of methods based on deep learning in image analysis, it appears that they can be effective in digital pathology to support the work of pathologists. However, a major limitation in the development of computer-aided diagnostic systems for pathology is the cost of data annotation. Evaluation of tissue (histopathological) and cellular (cytological) specimens seems to be a complex challenge. To simplify the laborious process of obtaining a sufficiently large set of data, a number of different systems could be used for image annotation. Some of these systems are reviewed in this paper with a comparison of their capabilities.

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.

Sjőgren’s Syndrome is a systemic disease, presenting itself in a spectrum of symptoms and signs throughout the body. Its diagnosis can include the examination of minor salivary gland biopsies. The image processing of digital images, specifically large-scale Whole Slide Images can aid in diagnosis, but unfortunately requires a lot of processing power and time for automatic analysis. This paper presents a new method for fast foreground (tissue) segmentation in fragments of Whole Slide Images containing biopsies of minor salivary glands taken from patients with diagnosed Sjőgren’s Syndrome. The method involves training a neural network with a novel type of layer, which calculates Fourier transform of data within the network. The neural network achieved accuracy of 89% on a test dataset.

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 (ALNsWe 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.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 ALNsThe 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

Diffuse large B-cell lymphoma (DLBCL) is a fast-growing and aggressive neoplasm originating from B lymphocytes. Evaluation of proliferation index (PI) based on Ki67 immunohistochemical nuclear staining is used to distinguish proliferating (immunopositive) from nonproliferating (immunonegative) lymphoma cells. Human interpretation of PI varies and is time-consuming, therefore automatic computer-assisted approach may facilitate the performance. Herein we propose a new fully automatic proliferation index estimation (FLAPIE) algorithm, dedicated to detection of immunopositive and immunonegative nuclei, and evaluation of PI in digital microscopy images of DAB&H-stained samples from patients with high-grade DLBCL. FLAPIE performs nuclei detection in original RGB colour space and is independent of image brightness due to its textural-statistical approach. Validation of FLAPIE was performed in 61 non-overlapping whole-slide image fragments and compared to the results of PI estimation by QuPath open-source software, MetPiKi algorithm and manual evaluation by two independent observers. Interobserver agreement was calculated between the nuclei count and PIs by two observers. High concordance was found between both DAB and H-stained nuclei count, and PIs by two observers. Compared to MetPiKi, FLAPIE presented improved results of DAB and H-stained nuclei detection. In contrary to MetPiKi and QuPath, FLAPIE performed nuclei detection in all images and its results closely matched the number of DAB-stained nuclei evaluated by two observers. No significant difference was found between PIs by all computational methods and observers. FLAPIE achieved good results in PI estimation and prospectively aims to serve as a tool for clinical application in support of patients selection and decision to treatment.

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.

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.

This paper presents the METINUS Plus (METhod of Immunohistochemical NUclei Segmentation Plus) method that has been developed for localization and quantification of the regulatory T cells’ nuclei. The proposed methodology performs color separation followed by the extraction and validation of objects. Objects categorized as clusters, based on the area and shape, are divided with locally applied watershed supported by color deconvolution. Objects are validated based on RGB, Lab color space, and color deconvolution data. The chosen validation criteria allow quantification and differentiation between the subpopulations of regulatory T cells and breast cancer cells, which express FOXP3. The evaluation is based on the quantity and quality of nuclei segmentation in comparison with the results of the manual counting, done by four pathologists on a set of 20 images. The results were analyzed in comparison with human evaluation using Kendall's tau-b correlation coefficient and the Wilcoxon signed-rank test. The main achievement of this study is a possibility to find criteria that allow differentiation between two types of immunopositive cells’ nuclei: regulatory T cells with homogeneous stained nuclei and all cells with FOXP3 expression; in both tumor and axillary node biopsies. A major advantage of computer image processing is the reproducibility of achieved results, thus minimizing human intervention, and providing traceable clinical information.

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.

Sjogren’s Syndrome is a systemic disease. Its diagnosis can be supported by histopathological examination of minor salivary glands. The value of the focus score equal or greater than one ensures the diagnosis of this disease. Human estimation is inherently subjective, which often leads to diverging results during the process of diagnosis. The paper proposes to use U-net CNN to find area of inflammation in WSI to support pathologist in the process of diagnosis by selecting foci for detailed inspection. Proposed neural network was trained based on tiles of size \(256\times 256\) pixels in magnification 400x from 13 digital slides stained with hematoxylin and eosin from patients with labial minor salivary gland biopsies. The ground truth was established by manual annotations done by a pathology resident. The accuracy and recall of proposed neural network model on testing dataset show the potential of machine learning for classification problem solving in this field.

Evaluating whole slide images of histological and cytological samples is used in pathology for diagnostics, grading and prognosis . It is often necessary to rescale whole slide images of a very large size. Image resizing is one of the most common applications of interpolation. We collect the advantages and drawbacks of nine interpolation methods, and as a result of our analysis, we try to select one interpolation method as the preferred solution. To compare the performance of interpolation methods, test images were scaled and then rescaled to the original size using the same algorithm. The modified image was compared to the original image in various aspects. The time needed for calculations and results of quantification performance on modified images were also compared. For evaluation purposes, we used four general test images and 12 specialized biological immunohistochemically stained tissue sample images. The purpose of this survey is to determine which method of interpolation is the best to resize whole slide images, so they can be further processed using quantification methods. As a result, the interpolation method has to be selected depending on the task involving whole slide images.

The aim of the project is to design and to implement a web-based platform for the computer analysis of microscopic images which support the pathological diagnosis. The use of the platform will be free of charge. It offers: quantitative analysis of staining tissue sample’ images, archiving microscopic images, peer consultation, and join work independently from distance between scientific collaborating centers to registered doctors, scientists and students. The use of proposed platform allows: (i) to save pathologists’ time spend on quantitative analysis, (ii) to reduce consulting costs by replacing sending of the physical preparations by placing their images (mostly virtual slide) on the platform server, (iii) to increase reproducibility, comparability and objectivity of quantitative evaluations. These effects have a direct impact on improving the effectiveness and decreasing the costs of patients’ treatment. This paper presents the main ideas of the project which deliver web-based system working as multi-functional, integrated, modular and scalable computer system. The details of hardware solutions, concept of the workflow in the platform, the programming language and interpreters, the specific tools and algorithms, and the user interfaces are described below. The practical solutions for web-based services in the area of medical image analysis, storage and retrieval are also presented and discussed.

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 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.

Quantitative analysis of histopathological sections can be used to support the diagnosis and evaluate the disease progression by pathologists. The use of computer-aided diagnosis in pathology can substantially enhance the efficiency and accuracy of pathologists decisions, and overall benefit the patient. The evaluation of the shape of specific types of cell nuclei plays an important role in histopathological examination in various types of cancer. In this study we try to verify how much the results of segmentation could be improved with applying boundary refinement algorithm to thresholded histopathological image. In this paper we studied 5 methods based on various approaches: active contour, k-means clustering, and region-growing. For evaluation purposes, ground truth templates were generated by manual annotation of images. The performance is evaluated using pixel-wise sensitivity and specificity metrics. It appears that satisfactory results were achieved only by two algorithms based on active contour. By applying methodology based on active contour algorithm we managed to achieve sensitivity of about 93% and specificity of over 99%. To sum up, thresholding algorithms produce results that almost never perfectly fit to real object’s boundary, but this initial detection of objects followed by boundary refinement results in more accurate segmentation.

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.

The aim of this study is the comparison of the movement of various types of human dermal fibroblasts with and without genetic modification. Three groups of fibroblasts were cultured and monitored: (1) control group of fibroblasts isolated from human skin (not transduced); (2) transduced with lentivirus bearing EGFP fluorescent marker; (3) transduced with lentivirus bearing DsRed2 fluorescent marker. The experimental sequences of images documenting cells’ movement have been analyzed using image processing methods. The cells’ movement was described by: (1) distance between each two consecutive images/frames, (2) distance and displacement covered by each fibroblast in 30 minutes (3) length of cell crawling cycle, (4) the movement tortuosity coefficient for all fibroblasts in each group. Also shape features such as: area, perimeter, eccentricity, length of the major axis have been analyzed. It appears that the general movement behavior is not changed by the process of transduction but some of its aspects are modified. The efficiency of movement in the sense of distance covered and region penetration is decreased because of changes in cells’ morphology. Transduced cells are less polarized and develop extra podia during their crawling.

Radiographs, which visualise human dentition as a single panorama photograph, are commonly used in prognosis and diagnosis in dental care and biometric individual identification. The automatics analysis of these images can provide useful information about patients. So, this paper proposes a method of an introductory phase of teeth segmentation: selection of a region of interest which contains every tooth in the pantomograph. The proposed method finds an area containing all the teeth and the line dividing the upper and lower jaws, called teeth line, using adaptive power-law transformation, Holder exponent, energy and entropy of the pantomograph. The teeth area is found correctly in 81.9%, while teeth line in 34.0%. The method is the introductory step in a single teeth segmentation, dentition evaluation according to presence of strong dental interventions and automatics generation of dental score for a patient.

A method of image equalisation that reduces non-uniformity of light distribution caused by optical devices and dust on camera sensors is presented. The method explores non-uniformity which occurs in archival images captured by a typical optical set which consists of a light microscope and a digital camera. A sufficient number of images with low density of foreground objects has been used to extract a global map of non-uniformity of the particular microscope and camera. The proposed method consists of two steps: – (1) extraction of the map of non-uniformity based upon a set of chosen images and – (2) correction of images acquired by the optical set. The global map is created based upon a modified value layer, the third layer of HSV colour space. The proposed method has been tested on images of immunohistochemically (IHC) stained samples of a biopsy tissue, and it has been validated using an image segmentation method developed earlier. The results of the light distribution equalization, as well as the equalized images segmentation turn out to be more similar to the reference method results (namely the manual counting results), than the results of the original images segmentation. The equalization method can be used for other types of images, but all of them should be acquired by the same optical set.

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.

Refining the information about the tissue is achieved by the standardization of color in whole-slide images and digital images of tissue sections. The investigation described in this paper proposes a solution to the problems of color dissimilarity for microscope slides digitalized by various equipment. A method of color standardization using color calibration microscope slide is proposed and a piece of software is provided. The proposed method is verified on microscope slides digitalized by various digitalization tools. It is shown that the resulting colors in two images of the same fragments are more similar to each other. The prepared microscope color calibration slide is adjusted to the standardization of immunohistochemically stained tissue samples but it can be easily adjusted to other types of staining. The software is independent from color, shape and location of color spots on the calibration slide so it can be easily used with the other calibration slides designed for different needs.