Fumikazu Kimura

Significance of cell proliferation markers (Minichromosome maintenance protein 7, topoisomerase IIα and Ki-67) in cavital fluid cytology: Can we differentiate reactive mesothelial cells from malignant cells?

The aim of this study was to evaluate whether immunocytochemical expressions of proliferation markers, such as minichromosome maintenance protein 7 (MCM 7), topoisomerase IIα (topo IIα), and Ki‐67, in reactive mesothelial cells and malignant cells obtained from cavital fluids could be useful for their differential diagnosis. Samples diagnosed as reactive mesothelial cells (14 cases) or malignant tumors (28 cases) in cavital fluids were examined. Immunocytochemical staining of MCM 7, topo IIα, and Ki‐67 was performed with the universal immunoperoxidase polymer method. In reactive mesothelial cells, MCM 7 was stained in a fine granular pattern and its distribution was uniform in the nuclei. Topo IIα and Ki‐67 were stained in a coarse granular pattern and the distributions were the same as MCM 7. In contrast, in malignant cells, MCM 7 was stained in an irregular and fine granular pattern, and topo IIα and Ki‐67 were stained in a uniform and coarse granular pattern. Labeling indices of MCM 7 (cut‐off value; 30%, sensitivity; 100%, and specificity; 100%), topo IIα (cut‐off value; 15%, sensitivity; 89.3%, and specificity; 92.9%) and Ki‐67 (cut‐off value; 30%, sensitivity; 64.3%, and specificity; 92.9%) of malignant cells were significantly higher than those of reactive mesothelial cells. MCM 7, topo IIα, and Ki‐67 are different types of cell proliferation markers. MCM 7 and topo IIα, in particular, could be reliable tools for differential diagnosis between reactive mesothelial cells and malignant cells. Diagn. Cytopathol. 2010.

Computational hepatocellular carcinoma tumor grading based on cell nuclei classification

Abstract. Hepatocellular carcinoma (HCC) is the most common histological type of primary liver cancer. HCC is graded according to the malignancy of the tissues. It is important to diagnose low-grade HCC tumors because these tissues have good prognosis. Image interpretation-based computer-aided diagnosis (CAD) systems have been developed to automate the HCC grading process. Generally, the HCC grade is determined by the characteristics of liver cell nuclei. Therefore, it is preferable that CAD systems utilize only liver cell nuclei for HCC grading. This paper proposes an automated HCC diagnosing method. In particular, it defines a pipeline-path that excludes nonliver cell nuclei in two consequent pipeline-modules and utilizes the liver cell nuclear features for HCC grading. The significance of excluding the nonliver cell nuclei for HCC grading is experimentally evaluated. Four categories of liver cell nuclear features were utilized for classifying the HCC tumors. Results indicated that nuclear texture is the dominant feature for HCC grading and others contribute to increase the classification accuracy. The proposed method was employed to classify a set of regions of interest selected from HCC whole slide images into five classes and resulted in a 95.97% correct classification rate.

Differential diagnosis of reactive mesothelial cells and malignant mesothelioma cells using the cell proliferation markers minichromosome maintenance protein 7, geminin, topoisomerase II alpha and Ki-67.

Objective: The aim of this study was to evaluate whether the immunocytochemical expression of cell proliferation markers, such as minichromosome maintenance protein 7 (MCM 7), geminin, topoisomerase II alpha (topo IIα) and Ki-67, which are different types of cell proliferation markers, could be useful for their differential diagnosis in reactive mesothelial cells and malignant mesothelioma cells obtained from body cavity fluids. Study Design: Samples diagnosed and later histologically confirmed as reactive mesothelial cells (39 cases) or malignant mesothelioma (32 cases) in body cavity fluids were examined. Immunocytochemical staining of MCM 7, geminin, topo IIα and Ki-67 was performed with the immunoperoxidase polymer method. Results: Labeling indices (LIs) of MCM 7 (cutoff value 20.0%; sensitivity 100%; specificity 100%), geminin (cutoff value 4.5%; sensitivity 88.0%; specificity 70.0%), topo IIα (cutoff value 11.0%; sensitivity 88.0%; specificity 92.0%) and Ki-67 (cutoff value 15.3%; sensitivity 78.0%; specificity 79.0%) of malignant mesothelioma cells were significantly higher than those of reactive mesothelial cells. Conclusion: LIs of MCM 7, geminin and topo IIα can be reliable tools for the differential diagnosis of reactive mesothelial cells and malignant mesothelioma cells.

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.

Automatic segmentation of hepatocellular structure from HE-stained liver tissue

The analysis of hepatic tissue structure is required for quantitative assessment of liver histology. Especially, a cord-like structure of liver cells, called trabecura, has important information in the diagnosis of hepatocellular carcinoma (HCC). However, the extraction of trabeculae is thought to be difficult because liver cells take on various colors and appearances depending on tissue conditions. In this paper, we propose an approach to extract trabeculae from images of hematoxyline and eosin stained liver tissue slide by extracting the rest of trabeculae: sinusoids and stromal area. The sinusoids are simply extracted based on the color information, where the image is corrected by an orientation selective filtering before segmentaion. The stromal area mainly consists of fiber, and often includes lymphocytes densely. Therefore, in the proposed method, fiber region and lymphocytes are extracted separately, then, stromal region is determined based on the extracted results. The determination of stroma is performed based on superpixels, to obtain precise boundaries. Once the regions of sinusoids and stroma are obtained, trabeculae can be segmented as the remaining region. The proposed method was applied to 10 test images of normal and HCC liver tissues, and the results were evaluated based on the manual segmentation. As a result, we confirmed that both sensitivity and specificity of the extraction of trabeculae reach around 90%.

Practical image quality evaluation for whole slide imaging scanner

Whole slide imaging (WSI) scanner scans pathological specimens to produce digital slides to use in pathology practice, research and computational pathology which enables monitor-based diagnosis and image analysis. However, the scanned image is sometimes insufficient in quality such as focusing-error and noise. Therefore, a quality evaluation method is obligatory for practical use of WSI system. In previous work, referenceless quality evaluation technique was proposed for this purpose but some artefacts (i.e. tissue-fold, air-bubble) in slide would also be detected as false positives, while they are useless. In this paper, we proposed a method for the practical system to assess WSI quality with eliminating false detection due to the artefacts. Firstly, support vector machine (SVM) was utilized for detecting ROIs with artefacts and then the image quality was evaluated excluding detected ROIs. Through the experiments, the effectiveness of proposed system has been demonstrated.

Analysis of quantitative phase obtained by digital holography on H&E-stained pathological samples

The application of digital holography in cell imaging is gaining attraction as it gives quantitative information related to optical thickness without the need for staining. In contrast, conventional pathology examination uses tissues or cells that are stained to visualize the morphological structure or molecular expression with color. However, the relationship between color information and quantitative phase inside histopathology specimen is not yet well understood. In this study, we developed a system to capture both a color image and digital hologram, and those of H&E (hematoxylin and eosin)-stained liver tissue were acquired. Then, we calculated and analyzed the relationship between the textural features inside the color and phase images for hepatocellular carcinoma (HCC) histopathological specimen. Upon experimental investigation, we found that gray-level co-occurrence matrix (GLCM) textural features in phase images are useful for discriminating cancer and normal tissue, and varies between HCC grades which bring the possibility to be utilized for HCC diagnosis or classification without staining procedure.

Application of digital holography on diagnosis of malignant lymphoma

We investigated phase distribution of normal and malignant lymphoma cells by using digital holography. The experimentally obtained phase image revealed distinct texture that has potential to be applied to malignant lymphoma identification.

Significance of Cytological Findings of Neuroblastomas: Rosette Arrangement and Neuropil Structure

Objectives: Improve treatment outcomes and clarify the biological characteristics of neuroblastoma, development of an international histological classification started several years ago. Aiming at the establishment of a cytology criteria corresponding to the new histological classification, we investigated a criteria comparing lesions related to neuroblastoma on referring to the morphological indices of neuroblastoma reported in the international classification. Methods: Several tumor specimens were investigated: 37 cases of neuroblastoma (undifferentiated type: 3, poorly differentiated type: 34), 3 cases of ganglioneuroblastoma (mixed type: 2, nodular type: 1), and one case of ganglioneuroma. Stamp cytology samples were prepared from cut surfaces of the tumors and then stained to the Papanicolaou method. Results: In neuroblastoma of the undifferentiated type, tumor cells contained a small oval nucleus with a high N/C ratio, showing a bare nucleus, and the nucleolus was distinct: no rosette formation or neutrophil was observed. In the poorly differentiated type, tumor cells showed a round-oval bare nucleus were scattered: rosette arrangement was observed in the background neuroblasts containing a bare nucleus. In ganglioneuroblastoma, immature neuroblasts showed a round-oval nucleus and large ganglion-like cells possessed a distinct nucleolus similar to poorly differentiated-type epithelial adenocarcinoma. Conclusion: In neuroblastoma, neutrophils were stained light green, and a partial Homer-Wright-type rosette arrangement was observed in the background. In the poorly differentiated type, tumor cells were generally large compared to those observed in the undifferentiated type. In ganglioneuroblastoma, cytological diagnosis can be relatively easily made when differentiated mature ganglion like cells are observed. In the case of surgery, a histological diagnosis of nervous system tumors is often performed using frozen sections, however tissue is usually damaged during freezing. Thus, cytology is more advantageous for diagnosis. The diagnostic accuracy can be improved utilizing the cytological characteristics of neuroblastic tumors.

MULTIFRACTAL COMPUTATION FOR NUCLEAR CLASSIFICATION AND HEPATOCELLULAR CARCINOMA GRADING

Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper pro- poses a textural feature descriptor and a novel computa- tional method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural fea- ture descriptor observes local and spatial characteristic s of the texture patterns by using multifractal computation. The textural features are utilized for nuclear segmentation, fi ber region detection, and liver cell nuclei classification. Fou r categories of nuclear features are computed such as texture, geometry, spatial distribution, and surrounding texture, for HCC classification. Significance of liver cell nuclei classi - fication method is evaluated by classifying non-neoplastic and tumor tissues. Furthermore, characteristics of the liv er cell nuclei were utilized for grading a set of HCC images into four classes and obtained 97.77% classification accu- racy.