A new method for segmentation of colour images applied to immunohistochemically stained cell nuclei

Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology

Purpose

Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides.

Design

This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing.

Results

Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area.

Conclusion

This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.

Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images

Computational image analysis is one means for evaluating digitized histopathology specimens that can increase the reproducibility and reliability with which cancer diagnoses are rendered while simultaneously providing insight as to the underlying mechanisms of disease onset and progression. A major challenge that is confronted when analyzing samples that have been prepared at disparate laboratories and institutions is that the algorithms used to assess the digitized specimens often exhibit heterogeneous staining characteristics because of slight differences in incubation times and the protocols used to prepare the samples. Unfortunately, such variations can render a prediction model learned from one batch of specimens ineffective for characterizing an ensemble originating from another site. In this work, we propose to adopt unsupervised domain adaptation to effectively transfer the discriminative knowledge obtained from any given source domain to the target domain without requiring any additional labeling or annotation of images at the target site. In this paper, our team investigates the use of two approaches for performing the adaptation: (1) color normalization and (2) adversarial training. The adversarial training strategy is implemented through the use of convolutional neural networks to find an invariant feature space and Siamese architecture within the target domain to add a regularization that is appropriate for the entire set of whole-slide images. The adversarial adaptation results in significant classification improvement compared with the baseline models under a wide range of experimental settings.

Segmenting overlapping cell nuclei in digital histopathology images

Automatic quantification of cell nuclei in immunostained images is highly desired by pathologists in diagnosis. In this paper, we present a new approach for the segmentation of severely clustered overlapping nuclei. The proposed approach first involves applying a combined global and local threshold method to extract foreground regions. In order to segment clustered overlapping nuclei in the foreground regions, seed markers are obtained by utilizing morphological filtering and intensity based region growing. Seeded watershed is then applied and clustered nuclei are separated. As pixels corresponding to stained cellular cytoplasm can be falsely identified as belonging to nuclei, a post processing step identifying positive nuclei pixels is added to eliminate these false pixels. This new approach has been tested on a set of manually labeled Tissue Microarray (TMA) and Whole Slide Images (WSI) colorectal cancers stained for the biomarker P53. Experimental results show that it outperformed currently available state of the art methods in nuclei segmentation.

Pathology imaging informatics for quantitative analysis of whole-slide images

Objectives

With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities.

Target audience

This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods.

Scope

First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

Multiscale integration of -omic, imaging, and clinical data in biomedical informatics

This paper reviews challenges and opportunities in multiscale data integration for biomedical informatics. Biomedical data can come from different biological origins, data acquisition technologies, and clinical applications. Integrating such data across multiple scales (e.g., molecular, cellular/tissue, and patient) can lead to more informed decisions for personalized, predictive, and preventive medicine. However, data heterogeneity, community standards in data acquisition, and computational complexity are big challenges for such decision making. This review describes genomic and proteomic (i.e., molecular), histopathological imaging (i.e., cellular/tissue), and clinical (i.e., patient) data; it includes case studies for single-scale (e.g., combining genomic or histopathological image data), multiscale (e.g., combining histopathological image and clinical data), and multiscale and multiplatform (e.g., the Human Protein Atlas and The Cancer Genome Atlas) data integration. Numerous opportunities exist in biomedical informatics research focusing on integration of multiscale and multiplatform data.

An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections

A fully automatic segmentation and morphological analysis algorithm for the analysis of microvessels from CD31 immunostained histological tumour sections is presented. Development of the algorithm exploited the distinctive hues of stained vascular endothelial cells, cell nuclei and background, to provide the seeds for a 'region-growing' method for object segmentation in the 3D hue, saturation, value (HSV) colour model. The segmented objects, identified as microvessels by CD31 immunostaining, were post-processed with three morphological tasks: joining separate objects that were likely to belong to a single vessel, closing objects that had a narrow gap around their periphery, and splitting objects with multiple lumina into individual vessels. The automatic segmentation was validated against a hand-segmented set of 44 images from three different SW1222 human colorectal carcinomas xenografted into mice. 96.3 ± 0.9% of pixels were found to be correctly classified. Automated segmentation was carried out on a further 53 images from three histologically distinct mouse fibrosarcomas (MFs) for morphological comparison with the SW1222 tumours. Four morphometric measurements were calculated for each segmented vessel: vascular area (VA), ratio of lumen area to vascular area (lu/VA), eccentricity (e), and roundness (ro). In addition, the total vascular area relative to tumour tissue area (rVA) was calculated. lu/VA, e and ro were found to be significantly smaller in MF tumours than in SW1222 tumours (p < 0.05; unpaired t-test). The algorithm is available through the website http://www.caiman.org.uk where images can be uploaded, processed and sent back to users. The output from CAIMAN consists of the original image with boundaries of segmented vessels overlaid, the calculated parameters and a Matlab file, which contains the segmentation that the user can use to derive further results.

Quantification of fluorescence intensity of labeled human mesenchymal stem cells and cell counting of unlabeled cells in phase-contrast imaging: an open-source-based algorithm

Assessment of cell fate is indispensable to evaluate cell-based therapies in regenerative medicine. Therefore, a widely used technique is fluorescence labeling. A major problem still is the standardized, noninvasive, and reliable quantification of fluorescence intensity of adherent cell populations on single-cell level, since total fluorescence intensity must be correlated to the cell number. Consequently, the aim of the present study was to produce and validate an open-source-based algorithm, capable of measuring the total fluorescence intensity of cell populations and assessing the total cell number in phase-contrast images. To verify the algorithms' capacity to assess fluorescence intensity, human mesenchymal stem cells were transduced to stably express enhanced green fluorescent protein and results produced by the algorithm were compared to flow cytometry analysis. No significant differences could be observed at any time (p ≥ 0.443). For validation of the algorithm for cell counting in phase-contrast images, adherent human mesenchymal stem cells were manually counted and compared to results produced by the algorithm (correlation coefficient [CC] r = 0.975), nuclei staining (CC r = 0.997), and hemocytometer (CC r = 0.629). We conclude that applying the developed algorithm in routine practice allows robust, fast, and reproducible assessment of fluorescence intensity and cell numbers in simple large-scale microscopy. The method is easy to perform and open source based.

Learning-based Method for P53 Immunohistochemically Stained Cell Image Segmentation

In this study, a learning-based color image conversion method is proposed for cell image segmentation. Firstly, we demonstrate that minimum distance-based pixel classification, such as clustering, for color image segmentation in the color space is equivalent to thresholding grayscale images. Motivated by this result, we develop the so called C-G-T procedure for color image segmentation, where color image (C) is first converted into grayscale (G) and thresholding (T) is then performed on the gray image to segment objects out of background. The transform for image conversion is learned from the global pixel distribution in the color space, while the threshold is learned from local pixel distribution of the gray image. The combination of global and local learning makes the C-G- T procedure adaptive and computational efficient. Extensive experiments are performed to verify the effectiveness of our method.

Analysis of protein expression in cell microarrays: a tool for antibody-based proteomics

Tissue microarray (TMA) technology provides a possibility to explore protein expression patterns in a multitude of normal and disease tissues in a high-throughput setting. Although TMAs have been used for analysis of tissue samples, robust methods for studying in vitro cultured cell lines and cell aspirates in a TMA format have been lacking. We have adopted a technique to homogeneously distribute cells in an agarose gel matrix, creating an artificial tissue. This enables simultaneous profiling of protein expression in suspension- and adherent-grown cell samples assembled in a microarray. In addition, the present study provides an optimized strategy for the basic laboratory steps to efficiently produce TMAs. Presented modifications resulted in an improved quality of specimens and a higher section yield compared with standard TMA production protocols. Sections from the generated cell TMAs were tested for immunohistochemical staining properties using 20 well-characterized antibodies. Comparison of immunoreactivity in cultured dispersed cells and corresponding cells in tissue samples showed congruent results for all tested antibodies. We conclude that a modified TMA technique, including cell samples, provides a valuable tool for high-throughput analysis of protein expression, and that this technique can be used for global approaches to explore the human proteome.

An image analysis-based method for quantification of chronic allograft damage index parameters

Chronic allograft damage index (CADI) is a semi-quantitative histopathological score that predicts renal graft outcome. We aimed to develop an objective image analysis-based method for quantification of CADI parameters. Thirty-five kidney transplant biopsies were visually analyzed according to the original CADI criteria, and divided into normal, mildly, moderately and severely altered groups. Digital images of the same samples were then analyzed with IPLab software. Areas of inflammation and fibrosis measured using image analysis increased simultaneously with corresponding visual scores, although the difference between non-inflamed and mildly inflamed groups was not statistically significant. Area of normal tubuli decreased in the images of samples with visually mild/moderate tubular atrophy and tended to be even smaller in the group with severe tubular atrophy. Image analysis-based glomerular sclerosis score increased concurrently with increasing visual score. Mesangial matrix increase score in image analysis was greater in the samples with visually mild/moderate mesangial matrix increase score compared to those with normal glomeruli, and it was highest in the group with severe mesangial matrix increase. An image analysis-based CADI scoring of renal allograft biopsies could provide more precise data for scientific studies, and help pathologists in renal allograft biopsy scoring.

Supervised learning-based cell image segmentation for p53 immunohistochemistry

In this paper, we present two new algorithms for cell image segmentation. First, we demonstrate that pixel classification-based color image segmentation in color space is equivalent to performing segmentation on grayscale image through thresholding. Based on this result, we develop a supervised learning-based two-step procedure for color cell image segmentation, where color image is first mapped to grayscale via a transform learned through supervised learning, thresholding is then performed on the grayscale image to segment objects out of background. Experimental results show that the supervised learning-based two-step procedure achieved a boundary disagreement (mean absolute distance) of 0.85 while the disagreement produced by the pixel classification-based color image segmentation method is 3.59. Second, we develop a new marker detection algorithm for watershed-based separation of overlapping or touching cells. The merit of the new algorithm is that it employs both photometric and shape information and combines the two naturally in the framework of pattern classification to provide more reliable markers. Extensive experiments show that the new marker detection algorithm achieved 0.4% and 0.2% over-segmentation and under-segmentation, respectively, while reconstruction-based method produced 4.4% and 1.1% over-segmentation and under-segmentation, respectively.

“Virtual flow cytometry” of immunostained lymphocytes on microscopic tissue slides:iHCFlow™ tissue cytometry

BACKGROUND

A method and approach is developed for fully automated measurements of immunostained lymphocytes in tissue sections by means of digital color microscopy and patent pending advanced cell analysis. The validation data for population statistic measurements of immunostained lymphocytes in tissue sections using tissue cytometry (TC) is presented. The report is the first to describe the conversion of immunohistochemistry (IHC) data to a flow cytometry-like two parameter dot-plot display, hence the technique is also a virtual flow cytometry. We believe this approach is a paradigm shift, as well as novel, and called the system iHCFlow TC. Seven issues related to technical obstacles to virtual flow cytometry (FC) are identified.

DESIGN

Segmentation of a 512 x 474 RGB image and tabular display of statistical results table took 12-15 s using proprietary developed algorithms. We used a panel of seven antibodies for validation on 14 cases of mantle cell lymphoma giving percentage positive, total lymphocytes, and staining density. A total of 2,027 image frames with 810,800 cell objects (COBs) were evaluated. Antibodies to CD3, CD4, CD8, Bcl-1, Ki-67, CD20, CD5 were subjected to virtual FC on tissue. The results of TC were compared with manual counts of expert observers and with the results of flow cytometric immunophenotyping of the same specimen.

RESULTS

The correlation coefficient and 95% confidence interval by linear regression analysis yielded a high concordance between manual human results (M), FC results, and TC results per antibody, (r = 0.9365 M vs. TC, r= 0.9537 FC vs. TC). The technical issues were resolved and the solutions and results were evaluated and presented.

CONCLUSION

These results suggest the new technology of TC by iHCFlow could be a clinically valid surrogate for both M and FC analysis when only tissue IHC is available for diagnosis and prognosis. The application for cancer diagnosis, monitoring, and prognosis is for objective, rapid, automated counting of immunostained cells in tissues with percentage results. We report a new paradigm in TC that converts IHC staining of lymphocytes to automated results and a flow cytometry-like report. The dot plot histogram display is familiar, intuitive, informative, and provides the pathologists with an automated tool to rapidly characterize the staining and size distribution of the immunoreactive as well as the negative cell population in the tissue. This systems tool is a major improvement over existing ones and satisfies fully the criteria to perform Cytomics (Ecker and Tarnok, Cytometry A 2005;65:1; Ecker and Steiner, Cytometry A 2004;59:182-190; Ecker et al., Cytometry A 2004;59:172-181).

Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.

Automatic analysis of immunocytochemically stained tissue samples

An automatic colour image segmentation and cell counting software system has been developed for immunocytochemical analysis of stained tissue samples. The system was designed to count the total number of positive and negative cells in tissue samples treated with cytokine DNA probes from pigs naturally parasitised with Taenia solium metacestodes, using in situ hybridisation. A reaction index was calculated as the ratio of the number of cells with a positive reaction to the total number of cells (positives plus negatives) for each of five different probes. The objectives of automatic counting were to improve the reproducibility of the analysis and reduce the processing time of large image batches. A fast KNN classifier was used for colour segmentation. Watershed segmentation combined with edge detection was used to isolate individual cells that were then automatically labelled, using the results of the corresponding colour segmented image. Validation was performed on 122 non-training digital images with a total of 1069 positive cells and 1459 negative cells, with the following results: a mean true positive rate of 90.2% for positive cells and a mean true positive rate of 85.4% for negative cells. The corresponding mean false positive rates were 9.6% and 6.6%. The mean reaction index error of the automatic analysis was 5.35%. The processing of each digital image took 10 s on a Pentium IV PC.

Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation

BACKGROUND

Rac1 is a GTP-binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist-induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells.

METHODS

A fully automatic image analysis method for cell segmentation, feature extraction, and classification of cells according to their activation, i.e., GFP-Rac1 translocation and ruffle formation at stimuli, is described. Based on training data produced by visual annotation of four image series, a statistical classifier was created.

RESULTS

The results of the automatic classification were compared with results from visual inspection of the same time sequences. The automatic classification differed from the visual classification at about the same level as visual classifications performed by two different skilled professionals differed from each other. Classification of a second image set, consisting of seven image series with different concentrations of agonist, showed that the classifier could detect an increased proportion of activated cells at increased agonist concentration.

CONCLUSIONS

Intracellular activities, such as ruffle formation, can be quantified by fully automatic image analysis, with an accuracy comparable to that achieved by visual inspection. This analysis can be done at a speed of hundreds of cells per second and without the subjectivity introduced by manual judgments.

Image analysis system for assessment of immunohistochemically stained proliferative marker (MIB-1) in oesophageal squamous cell carcinoma

The prognosis of oesophageal cancer patients is related to the portion of MIB-1 positively stained tumour nuclei. In this study, an image analysis system was developed based on LEICA Image Processing and Analysis System to reduce the subjective, tedious and inaccurate manual counting of nuclei staining. Representative oesophageal cancer tissues were collected and immunohistochemical preparations of MIB-1 were made. The MIB-1 positive nuclei in these tumours were assessed by quantitative counting, semi-quantitative counting, and three computer assessment methods using LEICA QWIN PRO. Our results showed that computer assessment methods were reliable and consistent. The procedure using the system could be accomplished within 15 min. Overlapped or missed counting of nuclei by the observer were eliminated. The image analysis system can really assist experts in obtaining reliable data for the prognosis of oesophageal cancer patients quickly.

Increased expression of adenylyl cyclase isoforms in the adrenal gland of diabetic Goto-Kakizaki rat

The spontaneously diabetic Goto-Kakizaki rat harbors the same defects expressed in human type 2 diabetes. It is not clear, however, whether stress factors emanating from the adrenal glands are involved in causing the diabetic state. For that reason, the authors studied gland size and expression of adenylyl cyclase isoforms in adrenal glands from Goto-Kakizaki and normal rats. Goto-Kakizaki rat adrenals were found to weigh only about half as much as those of control rats. This decrease was the result of a reduction of the cortex, especially of the zona fasciculata, whereas the medulla was unaffected. Cell density measurements showed that the total number of medullary cells in Goto-Kakizaki rats was lower than that in controls. In the cortex, the cell density did not differ between the two groups; thus, our results point to a marked hypotrophy. In the medulla of Goto-Kakizaki rats, the nuclear size was significantly increased, and there was also an overexpression of adenylyl cyclase 1, 2, 4, 6, and 8 isoforms in the adrenalin-producing cells, indicating an increased functional capacity. In the cortex, despite the cortical hypotrophy, adenylyl cyclase 5 immunoreactivity was markedly increased in Goto-Kakizaki rats, especially in the zona reticularis. It is unclear whether this morphologic change in the diabetic adrenal glands together with the overexpression of different adenylyl cyclase isoforms plays a role in the pathogenesis of this diabetic state or is a genetic defect or compensatory mechanism of diabetes in this spontaneous rodent model of type 2 diabetes.

Effect of linomide on gut immune cell distribution and on TNF-alpha in plasma and ascites: an experimental study in the septic rat

A significant reduction of the pan T lymphocytes as well as CD4+ and CD8 subsets of cells in the gut mucosa of the septic rats has previously been demonstrated. In contrast, the populations of major histocompatibility complex (MHC) class II-positive cells and macrophages increased. The aim of this study was to evaluate if the immunomodulator Linomide influenced the immune cell distribution in the small intestinal mucosa in sepsis and, furthermore, if these changes coincide with changes in the concentration of tumor necrosis factor-alpha (TNF-alpha) in plasma or ascites. Polymicrobial sepsis was induced in rats by cecal ligation and puncture (CLP). Three different experimental groups were used: CLP, Linomide p.o. + CLP, and Linomide i.p.+ CLP, with adequate controls. Specimens were taken from the small bowel for immunohistologic staining and grading of mucosal injury. The following monoclonal antibodies were used: W3/25, OX8, R73, OX6, and ED1. All slides were examined by one "blinded" examiner. Mucosal injury was graded from 0 to 5. The immunostained tissues were also analyzed by an automatic color-based image system. All controls had a normal appearance of the mucosa (grade 0-1), whereas the septic animals had a median grade of III (II-IV) mucosal injury. Linomide i.p. + CLP decreased mucosal damage to median I (0-IV, P < 0.05). Linomide had no effects on the immune cell distribution in controls. In CLP rats, a significant reduction in both CD4+ and CD8+ T lymphocytes as well as an increased number of macrophages and MHC class II-positive cells was seen in the villi as compared with sham-operated controls (P < 0.05). Linomide attenuated these changes for CD8+ and T lymphocytes and macrophages. Sepsis caused increased concentrations of TNF-alpha in portal blood and ascites 3 h from CLP induction. This increase was attenuated by Linomide.

Early metastatic progression of bladder carcinoma: molecular profile of primary tumor and sentinel lymph node

PURPOSE

We characterized early metastatic progression of bladder carcinoma from the primary tumor, separated in the central part and invasive front, to the first lymphatic metastasis.

MATERIALS AND METHODS

Included in this study were 8 patients undergoing sentinel lymph node detection for invasive bladder cancer, of whom 4 had metastasis in the sentinel lymph node and 4 were randomly chosen without metastases. After microdissection p53 genomic structure and immunohistochemical expression of p53, pRB, Ki67 and E-cadherin were analyzed. Microvessel density and apoptosis were also assessed.

RESULTS

In 5 patients there were p53 gene mutations in the primary tumor, while 3 had the wild-type gene. The genotypes were identical in the central part and invasive front. All sentinel lymph node metastases harbored p53 mutations, in contrast to all nonmetastatic sentinel lymph nodes. Two patients had the same mutation as the primary tumor and 1 had an additional mutation. In a patient with a wild-type gene in each compartment of the primary tumor a mutation appeared in the corresponding sentinel lymph node metastasis. There was poor concordance of p53 mutation with protein status. The expression of p53, pRB, Ki67, E-cadherin, and the evaluation of apoptosis and angiogenesis showed in most cases only slight variations in tumor compartments and the sentinel lymph node.

CONCLUSIONS

In this study invasive bladder carcinoma involved monoclonal proliferations with a mainly homogenous biomarker profile. The first metastases in sentinel lymph nodes had a similar molecular profile but in half of the cases signs of clonal evolution appeared.

Down-regulation of CEACAM1 in human prostate cancer: correlation with loss of cell polarity, increased proliferation rate, and Gleason grade 3 to 4 transition

Many cancers have altered expression of various cell adhesion molecules. One of these is CEACAM1, which has been found to be downregulated in several carcinomas, including prostate cancer. We explored its immunohistochemical expression in a set of 64 total prostatectomy specimens and compared it with that of the epithelial cell adhesion molecule E-cadherin and occludin, a tight junction-associated molecule. The luminal surface of the epithelial cells of normal prostate glands and ducts showed a dense expression of CEACAM1. This pattern prevailed in prostate cancer of Gleason grades 1 to 3 as long as the cells maintained their polarity and formed individual glands. With "fusion" of glands (ie, in the transition to Gleason grade 4), the expression of CEACAM1 was lost in polygonal nonpolar cells and was lost or focally very weak in cells lining a lumen in the cribriform complexes. E-cadherin, which outlined the basolateral cell membranes of contacting neighboring epithelial cells was also downregulated in prostate carcinomas. However, the loss of E-cadherin expression in higher grades was gradual and not related to the Gleason 3 to >4 transition. Occludin was also lost in polygonal (ie, unpolarized) cells of Gleason grades 4 and 5, but remained expressed in all cells facing a lumen in all grades of cancer, which CEACAM1 was not. In conclusion, downregulation of CEACAM1 as well as that of occludin in prostate cancer is associated with loss of cell polarity. It coincides with the formation of the complex glandular architecture of Gleason grade 4 pattern or complete loss thereof in Gleason grade 5 patterns. The proliferative activity, measured as Ki67 labeling index, showed a fourfold increase in the carcinoma cells with lost CEACAM1 expression, supporting previous observations that CEACAM1 regulates cell proliferation. Immunohistochemical analysis of CEACAM1 expression patterns may be useful in assessment of the malignant potential of prostate carcinoma.

Improved recovery after spinal cord injury in neuronal nitric oxide synthase-deficient mice but not in TNF-alpha-deficient mice

Wild-type mice and mice lacking nitric oxide synthase (NOS) of neuronal type or TNF-alpha were subjected to an extradural compression of the thoracic spinal cord. The functional outcome of the hind limbs was assessed by using a motor function score (MFS). The injury resulted in paraplegia of the hind limbs in wild-type mice at day 1 after injury. Gradual recovery was observed during the following 14 days. Injured NOS -/- animals had an improved hind limb motor function during the entire observation period compared to wild-type controls. The difference was statistically significant on day 10 (p < 0.022) and day 14 (p < 0.048) after injury. At the site of injury, there was a trend of gray matter preservation in NOS -/- mice, as measured by MAP2 staining (p < 0.077). Injured mice lacking TNF-alpha had the lowest motor score among all the groups on day 1. During the following period, they had motor scores similar to those of wild-type controls and there was no significant difference at any time point. TNF-alpha -/- animals showed a trend of decreased white matter preservation compared to wild-type animals (p < 0.097). Our study shows that after spinal cord injury, mice lacking NOS have a better functional ability of their hind limbs than controls with the same degree of injury. This would indicate that the functional outcome is influenced in a negative way in wild mice by the presence of NO. The degree of secondary damage to the spinal cord might be attenuated in NOS-deficient mice.

Nuclear-labeling index analysis (NLIA), a software package used to perform accurate automation of cell nuclear-labeling index analysis on immunohistochemically stained rat liver samples

The nuclear labeling index (labeled nuclei/100 nuclei) and the apoptotic index (apoptotic cells/100 cells) are important parameters of cell growth and death. Automatic counting of labeled nuclei is desirable since manual counting is tedious, time-consuming, and with a greater potential for inaccuracies. A nuclear-labeling index analysis (NLIA) software package was developed in this laboratory to perform the counting process automatically and accurately. This software package consists of an application program NLIA and a set of macros for obtaining nuclear data that is used in Scion Image. It is designed to work cooperatively with Scion Image, Adobe Photoshop, and Microsoft Office. NLIA has two basic functions: building nuclear data files and analyzing nuclear data. A color image captured from an immunohistochemically stained or autoradiographic sample is loaded into NLIA. Nuclear data can be entered into the program manually, automatically, or in combination. In the manual data entering mode, NLIA acts as an object-counting tool, while in the automatic mode it acts as a data picker: picking up the data generated by Scion Image into memory. A method to enter nuclear data (both labeled nuclei and unlabeled nuclei) in the automatic mode is described. The color image is processed in Adobe Photoshop, where the interested color ranges are selected and separated. These are then analyzed in Scion Image with the help of the macros for obtaining nuclear data. Since the advanced particle analysis function is used, the counting process is automatic and rapid. Data from thousands of nuclei can be obtained within seconds. To ensure the accuracy of the analysis, a nuclear data checking and edit feature is employed in NLIA: results of computer-generated counting can be compared with the original color image by overlaying the plot of counting results onto the original color image. In this way any computer counting mistakes can be easily discovered and corrected by the operator. Corrected nuclear data (including nuclear size, location, shape) are then stored in data files. These data files can be used in NLIA to obtain cell density and nuclear labeling indices. Because criteria for obtaining nuclear data (truncation diameter, shape factor) can be set by the operator in NLIA, nuclear size distribution and shape variation can be analyzed. This method provides a fast and accurate way to determine cell nuclear-labeling indices. Currently, Scion Image is a freeware on the internet, and NLIA software package is available from our lab home page. Methods presented here expand the Scion Image ability to analyze color images by using color separation techniques in a commercial graphic application. The instrumentation required can be relatively inexpensive, and the methods described may be useful in studies of cell kinetics, lesion growth, and tumor therapy.

Spinal cord injury in ICAM-1-deficient mice: assessment of functional and histopathological outcome

Adhesion molecule-mediated adhesion and extravasation of leukocytes may constitute a mechanism of secondary tissue damage following spinal cord injury (SCI). The objective of the present study was to determine to what extent genetic deficiency in the adhesion molecule ICAM-1 influences functional and histopathological measures of outcome following SCI. ICAM-1-/- (n = 11) and wild-type (n = 9) mice were subjected to a compression-type SCI. Assessment of hind-limb motor function was done on days 1, 2, 4, 7, 10, and 14 after injury, using a motor function scoring system. Injury resulted in a drastically impaired hind limb motor function at day one after injury followed by a partial recovery during the observation period. No significant functional differences were found between the experimental groups at any time-point. Fourteen days after injury the animals were sacrificed and the spinal cords were processed for histopathological and immunohistochemical evaluation. Luxol-stained, MAP2-, GFAP- and iba-1-immunostained cross-sectional areas were quantitated using a computerized image analysis system to investigate white matter damage, neuronal loss, astrocytic response and microglial activation respectively. None of these parameters differed significantly between the groups. Separate experiments revealed that the early (24 h postinjury) infiltration of polymorphonuclear leukocytes was significantly reduced in white matter but not in the grey matter of ICAM-1-/- mice, compared to injured controls. In summary, these results do not support the concept that ICAM-1 alone mediates secondary tissue damage following traumatic SCI in the mouse.

Automatic quantification of microvessel density in urinary bladder carcinoma

Seventy-three TUR-T biopsies from bladder carcinoma were evaluated regarding microvessel density, defined as microvessel number (nMVD) and cross-section endothelial cell area (aMVD). A semi-automatic and a newly developed, automatic image analysis technique were applied in immunostainings, performed according to an optimized staining protocol. In 12 cases a comparison of biopsy material and the corresponding cystectomy specimen were tested, showing a good correlation in 11 of 12 cases (92%). The techniques proved reproducible for both nMVD and aMVD quantifications related to total tumour area. However, the automatic method was dependent on high immunostaining quality. Simultaneous, semi-automatic quantification of microvessels, stroma and epithelial fraction resulted in a decreased reproducibility. Quantification in ten images, selected in a descending order of MVD by subjective visual judgement, showed a poor observer capacity to estimate and rank MVD. Based on our results we propose quantification of MVD related to one tissue compartment. When staining quality is of high standard, automatic quantification is applicable, which facilitates quantification of multiple areas and thus, should minimize selection variability.

Parenchymal cell proliferation in coronary arteries after percutaneous transluminal coronary angioplasty: a human tissue bank study

PURPOSE

Restenosis after percutaneous transluminal coronary angioplasty (PTCA) remains a limitation of this technique. Arterial wall cell proliferation is a component of restenosis preventable with intravascular brachytherapy. This study attempts to locate the sites of cellular proliferation after PTCA so as to aid the optimization of this therapy.

METHODS AND MATERIALS

Autopsy records from January 1, 1985 through December 31, 1995 were reviewed, and 27 patients who received PTCA prior to death were identified who also had evidence of PTCA on histologic examination of the arterial sections. The sections were subjected to immunohistochemical staining for proliferating cell nuclear antigen (PCNA) to detect the proliferating cells in the arterial sections, followed by image analysis to determine the proliferative index (PI) of all regions and layers of the section.

RESULTS

The PI did not differ significantly according to vessel region (plaque, plaque shoulder, or portion of vessel wall with lowest plaque burden), vessel layer (intima, media, adventitia), or evidence of prior PTCA. There was a trend toward a higher PI in young lesions.

CONCLUSION

Cell proliferation in the vascular wall after PTCA was found throughout the treated arterial section's axial plane, not only in the periluminal region.