Tomoharu Kiyuna

Alteration of chromosome positioning during adipocyte differentiation

Chromosomes are highly restricted to specific chromosome territories within the interphase nucleus. The arrangement of chromosome territories is non-random, exhibiting a defined radial distribution as well as a preferential association with specific nuclear compartments, which indicates a functional role for chromosome-territory organization in the regulation of gene expression. In this report, we focus on changes in adipocyte differentiation that are related to a specific chromosomal translocation associated with liposarcoma tumorigenesis, t(12;16). We have examined the relative and radial positioning of the chromosome territories of human chromosomes 12 and 16 during adipocyte differentiation, and detected a close association between the territories of chromosomes 12 and 16 in differentiated adipocytes, an association not observed in preadipocytes. Although further studies are required to elucidate the underlying reasons for the adipocyte-specific translocation of chromosomes 12 and 16, our observations indicate that alteration of relative chromosome positioning might play a key role in the tumorigenesis of human liposarcomas. In addition, these results demonstrate the potential impact of higher order chromatin organization on the epigenetic mechanisms that control gene expression and gene silencing during cell differentiation.

Multiple Equivalent Current Dipole Source Localization of Visual Event-Related Potentials During Oddball Paradigm With Motor Response

Event-related potentials (ERPs) during a visual oddball paradigm with button-pressing responses were recorded in 12 right-handed subjects from 32 scalp electrodes. The single equivalent current dipole (ECD) of the target C1 (weak occipito-parietal negativity from 30-80ms) was consistently located at the primary visual cortex. From the 4-ECD localization of the target P1/N1 (temporally coincident frontal positivity and occipito-temporal negativity), it was suggested that this complex reflected activities from distributed sources along both dorsal occipito-parietal and ventral occipito-temporal areas. The stable multiple ECD solutions for the target P3b were chosen as those including the left primary motor and/or sensorimotor dipole and satisfying goodness-of-fit (GOF) of more than 98% and confidence limit (CL) of less than 1mm. The obtained frontal dipoles were discussed in terms of visual working memory and sustained attention in reference to the previous PET, fMRI and MEG studies. The distributed multiple ECDs may suggest that P3 should be interpreted as being the embodiment of the cortico-limbic-thalamic network which involves Halgren and Marinkovics emotional and behavioral model and Mesulams attentional circuit.

Multiple dipole analysis of visual event-related potentials during oddball paradigm with silent counting.

In order to cope with the non-uniqueness of multiple equivalent current dipole source (ECD) solutions, a priori knowledge about P300 generators of visual event-related potentials (ERPs) during an oddball paradigm with silent counting task was incorporated into the multiple ECD localization method. Four-ECD solutions for the target P300 were selected which had the left frontal ECD. The rest of the ECDs were localized to the inferior parietal lobule, the hippocampal formation and subcortical region. By comparing the present results with those on the visual ERPs with button-pressing task, the P300 dipoles common to both the tasks were located at the frontal cortices, the hippocampal formation and the thalamus, suggesting that these structures are the main P300 generators.

Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization.

The authors have developed a PC-based multichannel electroencephalogram (EEG) measurement and analysis system. This system enables us (1) to simultaneously record a maximum of 64 channels of EEG data, (2) to measure three-dimensional positions of the recording electrodes, (3) to rapidly and precisely localize equivalent current dipoles (ECDs) responsible for the EEG data, and (4) to superimpose the localization results on magnetic resonance images. A new neural network and numerical analysis (NNN) approach to ECD localization is described which integrates a feedforward artificial neural network (ANN) and a numerical optimization (Powells hybrid) method. It was shown that the ANN method has the advantages of high-speed localization and noise robustness, because in this approach: (1) ECD parameters are immediately initialized from the recorded EEG data by the ANN and (2) ECD parameters are accurately refined by the hybrid method. Our multiple ECD localization method was applied to sensory evoked potentials and event-related potentials using the present system.

Visual event-related potentials during movement imagery and the dipole analysis

Visual event-related potentials during an oddball paradigm with movement imagery tasks were recorded in 10 right-handed subjects from 32 scalp electrodes. Rare targets and non-targets elicited early (P3e) and late (P3l) P300 components. In the P3e there was no difference between the rare target and non-target. In the right-imagery task the rare target P3l amplitude was larger than the rare non-target one, whereas the rare non-target P3l amplitude was larger than the rare target one in the left-imagery task. Some of the 4 equivalent current dipole (ECD) sources were located at the subcortical regions, the cerebellum and the cingulate cortex, common to the P3e and the P3l. Moreover, another P3e dipole was localized to the parietal regions, while that of the P3l dipoles to the contralateral premotor cortex. This difference between the P3e and P3l dipoles might reflect two different neural networks related with the transformation of coordinates from visual to motor space.

Characterization of chromatin texture by contour complexity for cancer cell classification

The purpose of this study is to investigate a new technique for image-based cancer cell classification and provide a more quantitative and objective characterization method for a diagnosis, which currently relies on qualitative and empirical judgment of pathologists. For this, a new method for chromatin texture characterization employing a new feature, contour complexity, is proposed and evaluated using nuclear images obtained from paraffin-wax embedded sections of human breast cancer on slides. The proposed feature is calculated on the basis of a contour length of nucleus obtained by setting different threshold values of intensity for a grayscale image, and it is a quantitative measure of chromatin texture. An expectation-maximization (EM) algorithm-based segmentation and an effective initial parameter search method for EM are used for the automatic calculation of the feature. The results for breast cancer cell detection showed that the average contour complexity value for malignant cells (19.6plusmn4.1) is found to be significantly greater (p < 10-6, Kolmogorov-Smirnov test) than that of benign cells (0.35plusmn0.17). By the comparison with the conventional fractal dimension approach, it is shown that the proposed feature is much more sensitive feature than the fractal dimension for the individual cancer cell detection.

Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

Diagnosis of hepatocellular carcinoma (HCC) on the basis of digital images is a challenging problem because, unlike gastrointestinal carcinoma, strong structural and morphological features are limited and sometimes absent from HCC images. In this study, we describe the classification of HCC images using statistical distributions of features obtained from image analysis of cell nuclei and hepatic trabeculae. Images of 130 hematoxylin-eosin (HE) stained histologic slides were captured at 20X by a slide scanner (Nanozoomer, Hamamatsu Photonics, Japan) and 1112 regions of interest (ROI) images were extracted for classification (551 negatives and 561 positives, including 113 well-differentiated positives). For a single nucleus, the following features were computed: area, perimeter, circularity, ellipticity, long and short axes of elliptic fit, contour complexity and gray level cooccurrence matrix (GLCM) texture features (angular second moment, contrast, homogeneity and entropy). In addition, distributions of nuclear density and hepatic trabecula thickness within an ROI were also extracted. To represent an ROI, statistical distributions (mean, standard deviation and percentiles) of these features were used. In total, 78 features were extracted for each ROI and a support vector machine (SVM) was trained to classify negative and positive ROIs. Experimental results using 5-fold cross validation show 90% sensitivity for an 87.8% specificity. The use of statistical distributions over a relatively large area makes the HCC classifier robust to occasional failures in the extraction of nuclear or hepatic trabecula features, thus providing stability to the system.

Automated histological classification of whole-slide images of gastric biopsy specimens

BackgroundAutomated image analysis has been developed currently in the field of surgical pathology. The aim of the present study was to evaluate the classification accuracy of the e-Pathologist image analysis software.MethodsA total of 3062 gastric biopsy specimens were consecutively obtained and stained. The specimen slides were anonymized and digitized. At least two experienced gastrointestinal pathologists evaluated each slide for pathological diagnosis. We compared the three-tier (positive for carcinoma or suspicion of carcinoma; caution for adenoma or suspicion of a neoplastic lesion; or negative for a neoplastic lesion) or two-tier (negative or non-negative) classification results of human pathologists and of the e-Pathologist.ResultsOf 3062 cases, 33.4% showed an abnormal finding. For the three-tier classification, the overall concordance rate was 55.6% (1702/3062). The kappa coefficient was 0.28 (95% CI, 0.26–0.30; fair agreement). For the negative biopsy specimens, the concordance rate was 90.6% (1033/1140), but for the positive biopsy specimens, the concordance rate was less than 50%. For the two-tier classification, the sensitivity, specificity, positive predictive value, and negative predictive value were 89.5% (95% CI, 87.5–91.4%), 50.7% (95% CI, 48.5–52.9%), 47.7% (95% CI, 45.4–49.9%), and 90.6% (95% CI, 88.8–92.2%), respectively.ConclusionsAlthough there are limitations and requirements for applying automated histopathological classification of gastric biopsy specimens in the clinical setting, the results of the present study are promising.

Enhancing automatic classification of hepatocellular carcinoma images through image masking, tissue changes and trabecular features

Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis.

Changes in chromatin structure during processing of wax-embedded tissue sections

The use of immunofluorescence (IF) and fluorescence in situ hybridisation (FISH) underpins much of our understanding of how chromatin is organised in the nucleus. However, there has only recently been an appreciation that these types of study need to move away from cells grown in culture and towards an investigation of nuclear organisation in cells in situ in their normal tissue architecture. Such analyses, however, especially of archival clinical samples, often requires use of formalin-fixed paraffin wax-embedded tissue sections which need addition steps of processing prior to IF or FISH. Here we quantify the changes in nuclear and chromatin structure that may be caused by these additional processing steps. Treatments, especially the microwaving to reverse fixation, do significantly alter nuclear architecture and chromatin texture, and these must be considered when inferring the original organisation of the nucleus from data collected from wax-embedded tissue sections.