Quantification of myocardial fibrosis by digital image analysis and interactive stereology

Oral ulcer treatment using human tonsil-derived mesenchymal stem cells encapsulated in trimethyl chitosan hydrogel: an animal model study

BACKGROUND

Oral ulcers are a common side effect of chemotherapy and affect patients' quality of life. While stem cell transplantation is a potential treatment for oral ulcers, its efficacy is limited as the stem cells tend to remain in the affected area for a short time. This study aims to develop a treatment for oral ulcers by using trimethyl chitosan (TMC) hydrogel with human tonsil-derived stem cells (hTMSCs) to increase the therapeutic effect of stem cells and investigate their effectiveness.

METHODS

Animals were divided into four experimental groups: Control, TMC hydrogel, hTMSCs, and hTMSCs loaded in TMC hydrogel (Hydrogel + hTMSCs) (each n = 8). Oral ulcers were chemically induced by anesthetizing the rats followed by injection of dilute acetic acid in the right buccal mucosa. After confirming the presence of oral ulcers in the animals, a single subcutaneous injection of 100 µL of each treatment was applied to the ulcer area. Histological analyses were performed to measure inflammatory cells, oral mucosal thickness, and fibrosis levels. The expression level of inflammatory cytokines was also measured using RT-PCR to gauge therapeutic the effect.

RESULTS

The ulcer size was significantly reduced in the TMC hydrogel + hTMSCs group compared to the control group. The stem cells in the tissue were only observed until Day 3 in the hTMSCs treated group, while the injected stem cells in the TMC Hydrogel + hTMSCs group were still present until day 7. Cytokine analysis related to the inflammatory response in the tissue confirmed that the TMC Hydrogel + hTMSCs treated group demonstrated superior wound healing compared to other experimental groups.

CONCLUSION

This study has shown that the adhesion and viability of current stem cell therapies can be resolved by utilizing a hydrogel prepared with TMC and combining it with hTMSCs. The combined treatment can promote rapid healing of oral cavity wounds by enhancing anti-inflammatory effects and expediting wound healing. Therefore, hTMSC loaded in TMC hydrogel was the most effective wound-healing approach among all four treatment groups prolonging stem cell survival. However, further research is necessary to minimize the initial inflammatory response of biomaterials and assess the safety and long-term effects for potential clinical applications.

QUANTITATIVE ASSESSMENT OF MYOCARDIAL FIBROSIS BY DIGITAL IMAGE ANALYSIS: an adjunctive tool for pathologist "ground truth": original article

AIMS

Myocardial fibrosis (MF) is a common pathological process in a wide range of cardiovascular diseases. Its quantity has diagnostic and prognostic relevance. We aimed to assess if the complementary use of an automated artificial intelligence software might improve the precision of the pathologist´s quantification of MF on endomyocardial biopsies (EMB).

METHODS AND RESULTS

Intraoperative EMB samples from 30 patients with severe aortic stenosis submitted to surgical aortic valve replacement were analysed. Tissue sections were stained with Masson´s trichrome for collagen/fibrosis and whole slide images (WSI) from the experimental glass slides were obtained at a resolution of 0.5μm using a digital microscopic scanner. Three experienced pathologists made a first quantification of MF excluding the subendocardium. After two weeks, an algorithm for Masson´s trichrome brightfield WSI (at QuPath software) was applied and the automatic quantification was revealed to the pathologists, who were asked to reassess MF, blinded to their first evaluation. The impact of the automatic algorithm on the inter-observer agreement was evaluated using Bland-Altman type methodology. Median values of MF on EMB were 8.33% [IQR 5.00-12.08%] and 13.60% [IQR 7.32-21.2%], respectively for the first pathologist´s and automatic algorithm quantification, being highly correlated (R²: 0.79; p<0.001). Inter-observer discordance was relevant, particularly for higher percentages of MF. The knowledge of the automatic quantification significantly improved the overall pathologist´s agreement, which became unaffected by the degree of MF severity.

CONCLUSIONS

The use of an automated artificial intelligence software for MF quantification on EMB samples improves the reproducibility of measurements by experienced pathologists. By improving the reliability of the quantification of myocardial tissue components, this adjunctive tool may facilitate the implementation of imaging-pathology correlation studies.

Adapted clustering method for generic analysis of histological fibrosis staining as an open source tool

Pathological remodeling of the extracellular matrix is a hallmark of cardiovascular disease. Abnormal fibrosis causes cardiac dysfunction by reducing ejection fraction and impairing electrical conductance, leading to arrhythmias. Hence, accurate quantification of fibrosis deposition in histological sections is of extreme importance for preclinical and clinical studies. Current automatic tools do not perform well under variant conditions. Moreover, users do not have the option to evaluate data from staining methods of their choice according to their purpose. To overcome these challenges, we underline a novel machine learning-based tool (FibroSoft) and we show its feasibility in a model of cardiac hypertrophy and heart failure in mice. Our results demonstrate that FibroSoft can identify fibrosis in diseased myocardium and the obtained results are user-independent. In addition, the results acquired using our software strongly correlate to those obtained by Western blot analysis of collagen 1 expression. Additionally, we could show that this method can be used for Masson's Trichrome and Picosirius Red stained histological images. The evaluation of our method also indicates that it can be used for any particular histology segmentation and quantification. In conclusion, our approach provides a powerful example of the feasibility of machine learning strategies to enable automatic analysis of histological images.

Automated quantification and statistical assessment of proliferating cardiomyocyte rates in embryonic hearts

The use of digital image analysis and count regression models contributes to the reproducibility and rigor of histological studies in cardiovascular research. The use of formalized computer-based quantification strategies of histological images essentially removes potential researcher bias, allows for higher analysis throughput, and enables easy sharing of formalized quantification tools, contributing to research transparency, and data transferability. Moreover, the use of count regression models rather than ratios in statistical analysis of cell population data incorporates the extent of sampling into analysis and acknowledges the non-Gaussian nature of count distributions. Using quantification of proliferating cardiomyocytes in embryonic murine hearts as an example, we describe how these improvements can be implemented using open-source artificial intelligence-based image analysis tools and novel count regression models to efficiently analyze real-life data.

In Vivo Comparison of Synthetic Macroporous Filamentous and Sponge-like Skin Substitute Matrices Reveals Morphometric Features of the Foreign Body Reaction According to 3D Biomaterial Designs

Synthetic macroporous biomaterials are widely used in the field of skin tissue engineering to mimic membrane functions of the native dermis. Biomaterial designs can be subclassified with respect to their shape in fibrous designs, namely fibers, meshes or fleeces, respectively, and porous designs, such as sponges and foams. However, synthetic matrices often have limitations regarding unfavorable foreign body responses (FBRs). Severe FBRs can result in unfavorable disintegration and rejection of an implant, whereas mild FBRs can lead to an acceptable integration of a biomaterial. In this context, comparative in vivo studies of different three-dimensional (3D) matrix designs are rare. Especially, the differences regarding FBRs between synthetically derived filamentous fleeces and sponge-like constructs are unknown. In the present study, the FBRs on two 3D matrix designs were explored after 25 days of subcutaneous implantation in a porcine model. Cellular reactions were quantified histopathologically to investigate in which way the FBR is influenced by the biomaterial architecture. Our results show that FBR metrics (polymorph-nucleated cells and fibrotic reactions) were significantly affected according to the matrix designs. Our findings contribute to a better understanding of the 3D matrix tissue interactions and can be useful for future developments of synthetically derived skin substitute biomaterials.

Methodological Progress of Stereology in Cardiac Research and Its Application to Normal and Pathological Heart Development

Design-based stereology is the gold standard for obtaining unbiased quantitative morphological data on volume, surface area, and length, as well as the number of tissues, cells or organelles. In cardiac research, the introduction of a stereological method to unbiasedly estimate the number of cardiomyocytes has considerably increased the use of stereology. Since its original description, various modifications to this method have been described. A particular field in which this method has been employed is the normal developmental life cycle of cardiomyocytes after birth, and particularly the question of when, during postnatal development, cardiomyocytes lose their capacity to divide and proliferate, and thus their inherent regenerative ability. This field is directly related to a second major application of stereology in recent years, addressing the question of what consequences intrauterine growth restriction has on the development of the heart, particularly of cardiomyocytes. Advances have also been made regarding the quantification of nerve fibers and collagen deposition as measures of heart innervation and fibrosis. In the present review article, we highlight the methodological progress made in the last 20 years and demonstrate how stereology has helped to gain insight into the process of normal cardiac development, and how it is affected by intrauterine growth restriction.

Intratumoral fibrosis and patterns of immune infiltration in clear cell renal cell carcinoma

Background

Intratumoral fibrosis was positively correlated with histological grade of renal clear cell carcinoma (ccRCC) and intratumoral inflammation. However, the association of intratumoral fibrosis with the immune infiltration of ccRCC was few evaluated.

Methods

We used the second harmonic generation (SHG)-based imaging technology and evaluated the intratumoral fibrosis in ccRCC, and then divided the patients into the high fibrosis group (HF) and the low fibrosis group (LF). Meanwhile, the Kaplan–Meier survival curve analysis was performed to analyze the relationship between intratumoral fibrosis and the disease-free survival rate. Antibody arrays were used for seeking difference in cytokines and immune infiltration between the HF group (N = 11) and LF group (N = 11). The selected immune infiltration marker was then verified by immunohistochemistry (IHC) staining in 45 ccRCC samples.

Results

Out of 640 cytokines and immune infiltration markers, we identified 115 proteins that were significantly different in quantity between ccRCC and adjacent normal tissues. In addition, the Venn diagram indicated that six proteins, including Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), were significantly associated with intratumoral fibrosis (p < 0.05). The GO/KEGG enrichment analysis indicated that the proteins associated with intratumoral fibrosis were involved in the immunity and tumor-infiltrating lymphocytes. The expression of the CTLA4 was negatively correlated with collagen level, confirmed by IHC staining of CTLA4 (p < 0.05).

Conclusions

The study indicated that the intratumoral fibrosis level was negatively correlated with the expression of CTLA4 in the tumor immune microenvironment of the ccRCC, which posed the potential value of targeting the stroma of the tumor, a supplement to immunotherapy. However, the specific mechanism of this association is still unclear and needs further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09765-0.

FIBER-ML, an Open-Source Supervised Machine Learning Tool for Quantification of Fibrosis in Tissue Sections

Pathologic fibrosis is a major hallmark of tissue insult in many chronic diseases. Although the amount of fibrosis is recognized as a direct indicator of the extent of disease, there is no consentaneous method for its quantification in tissue sections. This study tested FIBER-ML, a semi-automated, open-source freeware that uses a machine-learning approach to quantify fibrosis automatically after a short user-controlled learning phase. Fibrosis was quantified in sirius red-stained tissue sections from two fibrogenic animal models: acute stress-induced cardiomyopathy in rats (Takotsubo syndrome-like) and HIV-induced nephropathy in mice (chronic kidney disease). The quantitative results of FIBER-ML software version 1.0 were compared with those of ImageJ in Takotsubo syndrome, and with those of inForm in chronic kidney disease. Intra- and inter-operator and inter-software correlation and agreement were assessed. All correlations were excellent (>0.95) in both data sets. The values of discriminatory power between the pathologic and healthy groups were <10^-3 for data on Takotsubo syndrome and <10^-4 for data on chronic kidney disease. Intra-operator agreement, assessed by intra-class coefficient correlation, was good (>0.8), while inter-operator and inter-software agreement ranged from moderate to good (>0.7). FIBER-ML performed in a fast and user-friendly manner, with reproducible and consistent quantification of fibrosis in tissue sections. It offers an open-source alternative to currently used software, including quality control and file management.

QuPath Digital Immunohistochemical Analysis of Placental Tissue

Background:

QuPath is an open-source digital image analyzer notable for its user-friendly design, cross-platform compatibility, and customizable functionality. Since it was first released in 2016, at least 624 publications have reported its use, and it has been applied in a wide spectrum of settings. However, there are currently limited reports of its use in placental tissue. Here, we present the use of QuPath to quantify staining of G-protein coupled receptor 18 (GPR18), the receptor for the pro-resolving lipid mediator Resolvin D2, in placental tissue.

Methods:

Whole slide images of vascular smooth muscle (VSM) and extravillous trophoblast (EVT) cells stained for GPR18 were annotated for areas of interest. Visual scoring was performed on these images by trained and in-training pathologists, while QuPath scoring was performed with the methodology described herein.

Results:

Bland–Altman analyses showed that, for the VSM category, the two methods were comparable across all staining levels. For EVT cells, the high-intensity staining level was comparable across methods, but the medium and low staining levels were not comparable.

Conclusions:

Digital image analysis programs offer great potential to revolutionize pathology practice and research by increasing accuracy and decreasing the time and cost of analysis. Careful study is needed to optimize this methodology further.

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Semiautomated quantification of the fibrous tissue response to complex three-dimensional filamentous scaffolds using digital image analysis

Fibrosis represents a relevant response to the implantation of biomaterials, which occurs not only at the tissue-material interface (fibrotic encapsulation) but also within the void fraction of complex three-dimensional (3D) biomaterial constructions (fibrotic ingrowth). Usual evaluation of the biocompatibility mostly depicts fibrosis at the interface of the biomaterial using semiquantitative scores. Here, the relations between encapsulation and infiltrating fibrotic growth are poorly represented. Virtual pathology and digital image analysis provide new strategies to assess fibrosis in a more differentiated way. In this study, we adopted a method previously used to quantify fibrosis in visceral organs to the quantification of fibrosis to 3D biomaterials. In a proof-of-concept study, we transferred the "Collagen Proportionate Area" (CPA) analysis from hepatology to the field of biomaterials. As one task of an experimental animal study, we used CPA analysis to quantify the fibrotic ingrowth into a filamentous scaffold after subcutaneous implantation. We were able to demonstrate that the application of the CPA analysis is well suited as an additional fibrosis evaluation strategy for new biomaterial constructions. The CPA method can contribute to a better understanding of the fibrotic interactions between 3D scaffolds and the host tissue responses.

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.

Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring

Traditional histopathological allograft biopsy evaluation provides, within hours, diagnoses, prognostic information, and mechanistic insights into disease processes. However, proponents of an array of alternative monitoring platforms, broadly classified as "invasive" or "noninvasive" depending on whether allograft tissue is needed, question the value proposition of tissue histopathology. The authors explore the pros and cons of current analytical methods relative to the value of traditional and illustrate advancements of next-generation histopathological evaluation of tissue biopsies. We describe the continuing value of traditional histopathological tissue assessment and "next-generation pathology (NGP)," broadly defined as staining/labeling techniques coupled with digital imaging and automated image analysis. Noninvasive imaging and fluid (blood and urine) analyses promote low-risk, global organ assessment, and "molecular" data output, respectively; invasive alternatives promote objective, "mechanistic" insights by creating gene lists with variably increased/decreased expression compared with steady state/baseline. Proponents of alternative approaches contrast their preferred methods with traditional histopathology and: (1) fail to cite the main value of traditional and NGP-retention of spatial and inferred temporal context available for innumerable objective analyses and (2) belie an unfamiliarity with the impact of advances in imaging and software-guided analytics on emerging histopathology practices. Illustrative NGP examples demonstrate the value of multidimensional data that preserve tissue-based spatial and temporal contexts. We outline a path forward for clinical NGP implementation where "software-assisted sign-out" will enable pathologists to conduct objective analyses that can be incorporated into their final reports and improve patient care.

Digital Microscopy, Image Analysis, and Virtual Slide Repository

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Myofibrillolysis and fibrosis predicts myocardial insufficiency

Introduction

Cardiocyte myofibrillolysis and interstitial fibrosis belong to histopathological changes in cardiomyopathies, leading to heart failure.

Aim

To evaluate these changes in apical resection during left ventricular assist device (LVAD) implantation.

Material and methods

The studied group consisted of 40 patients with cardiomyopathy, and apical samples excised during left ventricular assist device implantation were studied (CM/VAD group, mean: 48.1 ±10 y/o). A control group consisted of 6 apical samples from healthy heart graft donors (mean: 29 ±2.3 years old). Area fraction (AF) was calculated for: fibrosis, cardiocytes with myofibrillolysis (MFL), non-myofibrillolytic cardiocytes (non-MFL).

Results

Single lymphocytes were seen in 18 (45%) cases in the CM/VAD group. Cardiomyopathy grade evaluated semiquantitatively in CM/VAD was: slight (25% of a group), moderate (35.5%), advanced (35.5%). CM/VAD cases showed nearly ten times higher fibrosis than the control group. The MFL cells occupied nearly a five times larger area in CM/VAD than in the control group, whereas non-MFL cells were found in the control group, as a predominant pattern. The linear regression calculated between fibrosis AF and types of cardiocytes indicated the depletion of cardiomyocytes with fibrosis increase. The control group presented insignificant dependency between fibrosis and MFL cells, suggesting the lack of replacement fibrosis. Significant negative dependence between fibrosis and non-MFL cardiocytes suggested remodeling in controls. Correlation analysis showed a strong relation between depletion of normal cardiocytes and progression of fibrosis.

Conclusions

Progression of cardiomyopathy and fibrosis depends on the loss of cardiocytes rather than degeneration of these cells.

Combination of Functional Magnetic Resonance Imaging and Histopathologic Analysis to Evaluate Interstitial Fibrosis in Kidney Allografts

BACKGROUND AND OBJECTIVES

Recent developments indicated that functional magnetic resonance imaging (MRI) could potentially provide noninvasive assessment of kidney interstitial fibrosis in patients with kidney diseases, but direct evidence from histopathology is scarce. We aimed to explore the diagnostic utilities of functional MRI for the evaluation of kidney allograft interstitial fibrosis.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We prospectively examined 103 kidney transplant recipients who underwent for-cause biopsies and 20 biopsy-proven normal subjects with functional MRI. Histomorphometric analyses of interstitial fibrosis and peritubular capillary densities were performed on digitally scanned Masson's trichrome- and CD34-stained slides, respectively. The performances of functional MRI to discriminate interstitial fibrosis were assessed by calculating the area under the curve using receiver-operating characteristic curve.

RESULTS

Main pathologic findings in this single-center cohort were representative of common diagnostic entities in the kidney allografts, with rejection (32%) and glomerulonephritides (31%) accounting for the majority of diagnoses. Apparent diffusion coefficient from diffusion-weighted imaging correlated with interstitial fibrosis (ρ=-0.77; P<0.001). Additionally, decreased arterial spin labelings were accompanied by peritubular capillary density reductions (r=0.77; P<0.001). Blood oxygen level-dependent (BOLD) imaging demonstrated cortical hypoxia with increasing interstitial fibrosis (ρ=0.61; P<0.001). The area under the curve for the discrimination of ≤25% versus >25% interstitial fibrosis and ≤50% versus >50% interstitial fibrosis were 0.87 (95% confidence interval [95% CI], 0.79 to 0.93) and 0.88 (95% CI, 0.80 to 0.93) by apparent diffusion coefficient, 0.92 (95% CI, 0.85 to 0.97) and 0.94 (95% CI, 0.87 to 0.98) by arterial spin labeling, 0.81 (95% CI, 0.72 to 0.88) and 0.86 (95% CI, 0.78 to 0.92) by perfusion fraction, 0.79 (95% CI, 0.69 to 0.87) and 0.85 (95% CI, 0.76 to 0.92) by BOLD imaging, respectively.

CONCLUSIONS

Functional MRI measurements were strongly correlated with kidney allograft interstitial fibrosis. The performances of functional MRI for discriminating ≤50% versus >50% interstitial fibrosis were good to excellent.

Aluminium foil dampened the adverse effect of 2100 MHz mobile phone-induced radiation on the blood parameters and myocardium in rats

Mobile phones emit a radiofrequency radiation (RFR) that might have adverse health effects. We aimed to investigate the possible protective effects of aluminium foil (AF) as a physical shield against the RFR from mobile phones on the blood parameters and the myocardium in rats. The effects of whole body 2100 MHz with 0.84-1.86 W/kg of SAR, 4 h/day for 30 days Global System for Mobile Communications (GSM)-RFR exposure for 4 h/day for 30 days on blood parameters (i.e. haemoglobin, leucocytes, thrombocytes, erythrocyte sedimentation rate, white blood cell differential count, corticosterone, CKMB), and the histology of myocardium were investigated. Three-month-old male rats (n = 32) were studied and randomised equally in the following four groups: K1 (non-AF non-RFR control), K2 (AF non-RFR control), P1 (non-AF RFR-exposed), P2 (AF RFR-exposed). Data were analysed with level of significance of p < 0.05. In P1, lower leucocytes and neutrophils counts with high corticosterone levels were found compared with the control groups, whilst a significantly higher CKMB was observed compared with P2 (p = 0.034). Lower cardiomyocyte counts congruent to the area fraction of the non-fibrotic myocardium were observed in P1 compared with the other groups (p < 0.01). AF might decrease the inflammatory-oxidative stress on rodent's blood cells and myocardium induced by the exposures of radiofrequency radiation of the mobile phones.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

Automatic stereology of mean nuclear size of neurons using an active contour framework

The use of unbiased stereology to quantify structural parameters such as mean cell and nuclear size (area and volume) can be useful for a wide variety of biological studies. Here we propose a novel segmentation framework using an Active Contour Model to automate the collection of stereology from stained cells and other objects in tissue sections. This approach is demonstrated for stained brain sections from young adult Fischer 344 rats. Animals were perfused in-vivo with 4% paraformaldehyde and sectioned by frozen microtomy at an instrument setting of 40 μm. For each rat brain, a systematic-random set of sections through the entire substantia nigra pars compacta (SN) were immunostained to reveal tyrosine hydroxylase (TH)-immunopositive neurons. The novel framework applied an active contour (modified balloon snake) model with non-constant balloon force to automatically segment and quantify neuronal cell bodies by stereological point counting (SPC). Several contours were initialized in the image and based on the contour fit after 200 iterations classified as immunopositive (signal) or background contours in a sequential manner. Cell contours were determined in four steps based on several criteria, e.g., area of contour, dispersion measure, and degree of overlap. The image was automatically segmented according to the final contours. Using a point grid automatically generated at systematic-random orientations over the images, points hitting the segmented neural cell bodies were automatically counted. The final values from the automatic framework were compared with findings for ground truth (manual SPC). The results of this study show a strong agreement between data collected by the automatic framework and the ground truth (R² ≥ 0.95) with a 5× gain in time efficiency for the automatic SPC. These findings give strong support for future applications of pattern recognition for assessing stereological parameters of biological objects identified by high signal:noise stains.

Role of moxifloxacin-dexamethasone in cardiac histomorphometric findings among Wistar rats from infected mothers

PURPOSE

To investigate cardiac changes in young rats, whose mothers underwent autogenic fecal peritonitis, during organogenesis phase and to evaluate the role of intravenous administration of moxifloxacin and dexamethasone in preventing infection-related cardiac changes.

METHODS

A prospective histomorphometric study was performed on 29 hearts of Wistar four-month old rats. Animals were divided into three groups: Negative Control Group (NCG) included 9 subjects from healthy mothers; Positive Control Group (PCG) included 10 subjects from mothers with fecal peritonitis (intra-abdominal injection of 10% autogenic fecal suspension in the gestational period) and did not receive any treatment; and Intervention Group (IG), with 10 animals whose infected mothers received moxifloxacin and dexamethasone treatment 24 hours after induction of fecal peritonitis.

RESULTS

Nuclear count was higher in the IG group as compared to PCG (p = 0.0016) and in NCG as compared to PCG (p = 0.0380). There was no significant difference in nuclear counts between NCG and IG.

CONCLUSION

Induced autogenic fecal peritonitis in pregnant Wistar rats determined myocardial changes in young rats that could be avoided by the early administration of intravenous moxifloxacin and dexamethasone.

Histopathologic features of alcoholic cardiomyopathy compared with idiopathic dilated cardiomyopathy

BACKGROUND

The histologic difference between alcoholic cardiomyopathy (ACM) and idiopathic dilated cardiomyopathy (IDCM) is unclear. The present study aimed to identify the quantitative pathologic features of ACM compared with IDCM.

METHODS

Specimens from 6 regions (anterior left ventricle [LV], lateral LV, inferior LV, interventricular septum [IVS], anterior right ventricle [RV], and inferior RV) were sampled from each explanted heart. Specimens from 4 healthy donor hearts were obtained as normal control. Tissues were sectioned and Masson trichrome stained. Histomorphometry was performed to evaluate the amount of myocyte, fibrosis, fatty tissue, and interstitium by Image-Pro Plus 6.0 (Media Cybernetics).

RESULTS

A total of 408 specimens were obtained from 34 ACMs and 34 IDCMs; 8 specimens were obtained from 4 healthy donor hearts. Compared to healthy donor hearts, we observed an increase in fibrosis which replaces myocytes in myocardium of end-stage cardiomyopathy. The overall myocyte ratio in myocardium was 69.5 ± 8.7% in ACM vs 71.9 ± 7.4% in IDCM (P < .05). The percentage of interstitium was 10.8 ± 4.8% in ACM vs 9.2 ± 6.2% in IDCM (P < .05). A significant difference of fibrosis, fatty tissue was not discovered. Moreover, the myocyte area was 65.37 ± 11.8% in ACM LV vs 70.03 ± 9.0% in IDCM LV (P < .001).

CONCLUSION

We described histologic characteristics in ACM and IDCM. There might be a quantitative difference of myocyte, interstitium in myocardium between ACM and IDCM, especially in LV. No difference was found in the percentage of fibrosis between the 2 groups.

Automated quantification of renal interstitial fibrosis for computer-aided diagnosis: A comprehensive tissue structure segmentation method

Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses. This study proposes an automated quantification system for measuring the amount of interstitial fibrosis in renal biopsy images as a consistent basis of comparison among pathologists. The system extracts and segments the renal tissue structures based on colour information and structural assumptions of the tissue structures. The regions in the biopsy representing the interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area and quantified as a percentage of the total area of the biopsy sample. A ground truth image dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated a good correlation in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification.

BACKGROUND AND OBJECTIVE

Interstitial fibrosis in renal biopsy samples is a scarring tissue structure that may be visually quantified by pathologists as an indicator to the presence and extent of chronic kidney disease. The standard method of quantification by visual evaluation presents reproducibility issues in the diagnoses due to the uncertainties in human judgement.

METHODS

An automated quantification system for accurately measuring the amount of interstitial fibrosis in renal biopsy images is presented as a consistent basis of comparison among pathologists. The system identifies the renal tissue structures through knowledge-based rules employing colour space transformations and structural features extraction from the images. In particular, the renal glomerulus identification is based on a multiscale textural feature analysis and a support vector machine. The regions in the biopsy representing interstitial fibrosis are deduced through the elimination of non-interstitial fibrosis structures from the biopsy area. The experiments conducted evaluate the system in terms of quantification accuracy, intra- and inter-observer variability in visual quantification by pathologists, and the effect introduced by the automated quantification system on the pathologists' diagnosis.

RESULTS

A 40-image ground truth dataset has been manually prepared by consulting an experienced pathologist for the validation of the segmentation algorithms. The results from experiments involving experienced pathologists have demonstrated an average error of 9 percentage points in quantification result between the automated system and the pathologists' visual evaluation. Experiments investigating the variability in pathologists involving samples from 70 kidney patients also proved the automated quantification error rate to be on par with the average intra-observer variability in pathologists' quantification.

CONCLUSIONS

The accuracy of the proposed quantification system has been validated with the ground truth dataset and compared against the pathologists' quantification results. It has been shown that the correlation between different pathologists' estimation of interstitial fibrosis area has significantly improved, demonstrating the effectiveness of the quantification system as a diagnostic aide.

The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth

CONTEXT

- Novel therapeutics often target complex cellular mechanisms. Increasingly, quantitative methods like digital tissue image analysis (tIA) are required to evaluate correspondingly complex biomarkers to elucidate subtle phenotypes that can inform treatment decisions with these targeted therapies. These tIA systems need a gold standard, or reference method, to establish analytical validity. Conventional, subjective histopathologic scores assigned by an experienced pathologist are the gold standard in anatomic pathology and are an attractive reference method. The pathologist's score can establish the ground truth to assess a tIA solution's analytical performance. The paradox of this validation strategy, however, is that tIA is often used to assist pathologists to score complex biomarkers because it is more objective and reproducible than manual evaluation alone by overcoming known biases in a human's visual evaluation of tissue, and because it can generate endpoints that cannot be generated by a human observer.

OBJECTIVE

- To discuss common visual and cognitive traps known in traditional pathology-based scoring paradigms that may impact characterization of tIA-assisted scoring accuracy, sensitivity, and specificity.

DATA SOURCES

- This manuscript reviews the current literature from the past decades available for traditional subjective pathology scoring paradigms and known cognitive and visual traps relevant to these scoring paradigms.

CONCLUSIONS

- Awareness of the gold standard paradox is necessary when using traditional pathologist scores to analytically validate a tIA tool because image analysis is used specifically to overcome known sources of bias in visual assessment of tissue sections.

Commentary

Historically, pathologists perform manual evaluation of H&E- or immunohistochemically-stained slides, which can be subjective, inconsistent, and, at best, semiquantitative. As the complexity of staining and demand for increased precision of manual evaluation increase, the pathologist’s assessment will include automated analyses (i.e., “digital pathology”) to increase the accuracy, efficiency, and speed of diagnosis and hypothesis testing and as an important biomedical research and diagnostic tool. This commentary introduces the many roles for pathologists in designing and conducting high-throughput digital image analysis. Pathology review is central to the entire course of a digital pathology study, including experimental design, sample quality verification, specimen annotation, analytical algorithm development, and report preparation. The pathologist performs these roles by reviewing work undertaken by technicians and scientists with training and expertise in image analysis instruments and software. These roles require regular, face-to-face interactions between team members and the lead pathologist. Traditional pathology training is suitable preparation for entry-level participation on image analysis teams. The future of pathology is very exciting, with the expanding utilization of digital image analysis set to expand pathology roles in research and drug development with increasing and new career opportunities for pathologists.

Quantitative analysis of myocardial tissue with digital autofluorescence microscopy

Background:

The opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit.

Methods:

This study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented.

Results:

It is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining.

Conclusion:

The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

Immuno-modification of enhancing stem cells targeting for myocardial repair

Despite the controversy in mechanism, rodent and clinical studies have demonstrated beneficial effects of stem/progenitor cell therapy after myocardial infarction (MI). In a rat ischaemic reperfusion MI model, we investigated the effects of immunomodification of CD 34⁺ cells on heart function and myocardial conduction. Bispecific antibody (BiAb), consisting of an anti-myosin light chain antibody and anti-CD45 antibody, injected intravenously was used to direct human CD34⁺ cells to injured myocardium. Results were compared to echocardiography guided intramyocardial (IM) injection of CD34⁺ cells and PBS injected intravenously. Treatment was administered 2 days post MI. Echocardiography was performed at 5 weeks and 3 months which demonstrated LV dilatation prevention and fractional shortening improvement in both the BiAb and IM injection approaches, with BiAb achieving better results. Histological analyses demonstrated a decrease in infarct size and increase in arteriogenesis in both BiAb and IM injection. Electrophysiological properties were studied 5 weeks after treatments by optical mapping. Conduction velocity (CV), action potential duration (APD) and rise time were significantly altered in the MI area. The BiAb treated group demonstrated a more normalized activation pattern of conduction and normalization of CV at shorter pacing cycle lengths. The ventricular tachycardia inducibility was lowest in the BiAb treatment group. Intravenous administration of BiAb offers an effective means of stem cell delivery for myocardial repair post-acute MI. Such non-invasive approach was shown to offer a distinct advantage to more invasive direct IM delivery.