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  1. Article ; Online: Meet the authors: Daisuke Komura and Shumpei Ishikawa.

    Komura, Daisuke / Ishikawa, Shumpei

    Patterns (New York, N.Y.)

    2023  Volume 4, Issue 7, Page(s) 100794

    Abstract: ... author Dr. Daisuke Komura and Principal Investigator Prof. Shumpei Ishikawa about their paper "Restaining ...

    Abstract In this People of Data, Cell Press Community Review Scientific Editor Leia Judge talks to lead author Dr. Daisuke Komura and Principal Investigator Prof. Shumpei Ishikawa about their paper "Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists," which was published in the February issue of
    Language English
    Publishing date 2023-07-14
    Publishing country United States
    Document type News
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2023.100794
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Immune repertoire profiling for disease pathobiology.

    Katoh, Hiroto / Komura, Daisuke / Furuya, Genta / Ishikawa, Shumpei

    Pathology international

    2022  Volume 73, Issue 1, Page(s) 1–11

    Abstract: Lymphocytes consist of highly heterogeneous populations, each expressing a specific cell surface receptor corresponding to a particular antigen. Lymphocytes are both the cause and regulator of various diseases, including autoimmune/allergic diseases, ... ...

    Abstract Lymphocytes consist of highly heterogeneous populations, each expressing a specific cell surface receptor corresponding to a particular antigen. Lymphocytes are both the cause and regulator of various diseases, including autoimmune/allergic diseases, lifestyle diseases, neurodegenerative diseases, and cancers. Recently, immune repertoire sequencing has attracted much attention because it helps obtain global profiles of the immune receptor sequences of infiltrating T and B cells in specimens. Immune repertoire sequencing not only helps deepen our understanding of the molecular mechanisms of immune-related pathology but also assists in discovering novel therapeutic modalities for diseases, thereby shedding colorful light on otherwise tiny monotonous cells when observed under a microscope. In this review article, we introduce and detail the background and methodology of immune repertoire sequencing and summarize recent scientific achievements in association with human diseases. Future perspectives on this genetic technique in the field of histopathological research will also be discussed.
    MeSH term(s) Humans ; B-Lymphocytes ; Neoplasms/genetics ; Neoplasms/metabolism ; High-Throughput Nucleotide Sequencing ; Receptors, Antigen, T-Cell/genetics ; Receptors, Antigen, T-Cell/metabolism
    Chemical Substances Receptors, Antigen, T-Cell
    Language English
    Publishing date 2022-11-07
    Publishing country Australia
    Document type Journal Article ; Review
    ZDB-ID 1194850-4
    ISSN 1440-1827 ; 1320-5463
    ISSN (online) 1440-1827
    ISSN 1320-5463
    DOI 10.1111/pin.13284
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Deep texture representation analysis for histopathological images.

    Herdiantoputri, Ranny Rahaningrum / Komura, Daisuke / Fujisaka, Kei / Ikeda, Tohru / Ishikawa, Shumpei

    STAR protocols

    2023  Volume 4, Issue 2, Page(s) 102161

    Abstract: ... execution of this protocol, please refer to Komura et al. (2022). ...

    Abstract Deep texture representations (DTRs) produced from a bilinear convolutional neural network allow objective quantification of tumor histopathology images effectively. They can be used for various analyses, including visualization of morphological correlation between histology images, content-based image retrieval (CBIR), and supervised learning. This protocol describes the simplified workflow to analyze DTRs from data preparation, visualization of the histological profile, and CBIR analysis, to supervised learning model development to predict the profile from histological images. For complete details on the use and execution of this protocol, please refer to Komura et al. (2022).
    Language English
    Publishing date 2023-03-23
    Publishing country United States
    Document type Journal Article
    ISSN 2666-1667
    ISSN (online) 2666-1667
    DOI 10.1016/j.xpro.2023.102161
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

    Ochi, Mieko / Komura, Daisuke / Onoyama, Takumi / Shinbo, Koki / Endo, Haruya / Odaka, Hiroto / Kakiuchi, Miwako / Katoh, Hiroto / Ushiku, Tetsuo / Ishikawa, Shumpei

    Scientific data

    2024  Volume 11, Issue 1, Page(s) 330

    Abstract: Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address ... ...

    Abstract Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address this issue, we introduce a comprehensive histopathology image dataset named PathoLogy Images of Scanners and Mobile phones (PLISM). The dataset consisted of 46 human tissue types stained using 13 hematoxylin and eosin conditions and captured using 13 imaging devices. Precisely aligned image patches from different domains allowed for an accurate evaluation of color and texture properties in each domain. Variation in PLISM was assessed and found to be significantly diverse across various domains, particularly between whole-slide images and smartphones. Furthermore, we assessed the improvement in domain shift using a convolutional neural network pre-trained on PLISM. PLISM is a valuable resource that facilitates the precise evaluation of domain shifts in digital pathology and makes significant contributions towards the development of robust machine learning models that can effectively address challenges of domain shift in histological image analysis.
    MeSH term(s) Humans ; Eosine Yellowish-(YS) ; Image Processing, Computer-Assisted/methods ; Machine Learning ; Neural Networks, Computer ; Staining and Labeling ; Histology ; Histological Techniques
    Chemical Substances Eosine Yellowish-(YS) (TDQ283MPCW)
    Language English
    Publishing date 2024-04-03
    Publishing country England
    Document type Dataset ; Journal Article
    ZDB-ID 2775191-0
    ISSN 2052-4463 ; 2052-4463
    ISSN (online) 2052-4463
    ISSN 2052-4463
    DOI 10.1038/s41597-024-03122-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Machine learning approaches for pathologic diagnosis.

    Komura, Daisuke / Ishikawa, Shumpei

    Virchows Archiv : an international journal of pathology

    2019  Volume 475, Issue 2, Page(s) 131–138

    Abstract: Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognition ... ...

    Abstract Machine learning techniques, especially deep learning techniques such as convolutional neural networks, have been successfully applied to general image recognitions since their overwhelming performance at the 2012 ImageNet Large Scale Visual Recognition Challenge. Recently, such techniques have also been applied to various medical, including histopathological, images to assist the process of medical diagnosis. In some cases, deep learning-based algorithms have already outperformed experienced pathologists for recognition of histopathological images. However, pathological images differ from general images in some aspects, and thus, machine learning of histopathological images requires specialized learning methods. Moreover, many pathologists are skeptical about the ability of deep learning technology to accurately recognize histopathological images because what the learned neural network recognizes is often indecipherable to humans. In this review, we first introduce various applications incorporating machine learning developed to assist the process of pathologic diagnosis, and then describe machine learning problems related to histopathological image analysis, and review potential ways to solve these problems.
    MeSH term(s) Humans ; Machine Learning ; Pathology, Clinical/methods
    Language English
    Publishing date 2019-06-20
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 1184867-4
    ISSN 1432-2307 ; 0945-6317
    ISSN (online) 1432-2307
    ISSN 0945-6317
    DOI 10.1007/s00428-019-02594-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ud III Zs.10: Show issues Location:
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  6. Article: Machine Learning Methods for Histopathological Image Analysis.

    Komura, Daisuke / Ishikawa, Shumpei

    Computational and structural biotechnology journal

    2018  Volume 16, Page(s) 34–42

    Abstract: Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to ... ...

    Abstract Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.
    Language English
    Publishing date 2018-02-09
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 2694435-2
    ISSN 2001-0370
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2018.01.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Viola: a structural variant signature extractor with user-defined classifications.

    Sugita, Itsuki / Matsuyama, Shohei / Dobashi, Hiroki / Komura, Daisuke / Ishikawa, Shumpei

    Bioinformatics (Oxford, England)

    2021  Volume 38, Issue 2, Page(s) 540–542

    Abstract: Summary: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi- ... ...

    Abstract Summary: Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g. cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data.
    Availability and implementation: Viola is available on pip (https://pypi.org/project/Viola-SV/) and the source code is on GitHub (https://github.com/dermasugita/Viola-SV).
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Humans ; Viola ; Software ; Neoplasms/genetics
    Language English
    Publishing date 2021-09-17
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btab662
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
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    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: Stomach encyclopedia: Combined single-cell and spatial transcriptomics reveal cell diversity and homeostatic regulation of human stomach.

    Tsubosaka, Ayumu / Komura, Daisuke / Kakiuchi, Miwako / Katoh, Hiroto / Onoyama, Takumi / Yamamoto, Asami / Abe, Hiroyuki / Seto, Yasuyuki / Ushiku, Tetsuo / Ishikawa, Shumpei

    Cell reports

    2023  Volume 42, Issue 10, Page(s) 113236

    Abstract: The stomach is an important digestive organ with various biological functions. However, because of the complexity of its cellular and glandular composition, its precise cellular biology has yet to be elucidated. In this study, we conducted single-cell ... ...

    Abstract The stomach is an important digestive organ with various biological functions. However, because of the complexity of its cellular and glandular composition, its precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequencing (scRNA-seq) and subcellular-level spatial transcriptomics analysis of the human stomach and constructed the largest dataset to date: a stomach encyclopedia. This dataset consists of approximately 380,000 cells from scRNA-seq and the spatial transcriptome, enabling integrated analyses of transcriptional and spatial information of gastric and metaplastic cells. This analysis identified LEFTY1 as an uncharacterized stem cell marker, which was confirmed through lineage tracing analysis. A wide variety of cell-cell interactions between epithelial and stromal cells, including PDGFRA
    MeSH term(s) Humans ; Transcriptome/genetics ; Gene Expression Profiling ; Stomach Neoplasms/genetics ; Carcinogenesis ; Single-Cell Analysis ; Sequence Analysis, RNA
    Language English
    Publishing date 2023-10-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2649101-1
    ISSN 2211-1247 ; 2211-1247
    ISSN (online) 2211-1247
    ISSN 2211-1247
    DOI 10.1016/j.celrep.2023.113236
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article: Machine Learning Methods for Histopathological Image Analysis

    Komura, Daisuke / Ishikawa, Shumpei

    Computational and Structural Biotechnology Journal. 2018, v. 16

    2018  

    Abstract: Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to ... ...

    Abstract Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.
    Keywords algorithms ; artificial intelligence ; biotechnology ; histopathology ; image analysis
    Language English
    Size p. 34-42.
    Publishing place Elsevier B.V.
    Document type Article
    ISSN 2001-0370
    DOI 10.1016/j.csbj.2018.01.001
    Database NAL-Catalogue (AGRICOLA)

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  10. Article ; Online: RNF213 p.Arg4810Lys Wild Type is Associated with De Novo Hemorrhage in Asymptomatic Hemispheres with Moyamoya Disease.

    Torazawa, Seiei / Miyawaki, Satoru / Imai, Hideaki / Hongo, Hiroki / Ishigami, Daiichiro / Shimizu, Masahiro / Ono, Hideaki / Shinya, Yuki / Sato, Daisuke / Sakai, Yu / Umekawa, Motoyuki / Kiyofuji, Satoshi / Shimada, Daisuke / Koizumi, Satoshi / Komura, Daisuke / Katoh, Hiroto / Ishikawa, Shumpei / Nakatomi, Hirofumi / Teraoka, Akira /
    Saito, Nobuhito

    Translational stroke research

    2023  

    Abstract: Clinical implications of RNF213 genetic variants, other than p.Arg4810Lys, in moyamoya disease (MMD), remain unclear. This study aimed to investigate the association of RNF213 variants with clinical phenotypes in MMD. This retrospective cohort study ... ...

    Abstract Clinical implications of RNF213 genetic variants, other than p.Arg4810Lys, in moyamoya disease (MMD), remain unclear. This study aimed to investigate the association of RNF213 variants with clinical phenotypes in MMD. This retrospective cohort study collected data regarding the clinical characteristics of 139 patients with MMD and evaluated the angioarchitectures of 253 hemispheres using digital subtraction angiography at diagnosis. All RNF213 exons were sequenced, and the associations of clinical characteristics and angiographical findings with p.Arg4810Lys, p.Ala4399Thr, and other rare variants (RVs) were examined. Among 139 patients, 100 (71.9%) had p.Arg4810Lys heterozygote (GA) and 39 (28.1%) had the wild type (GG). Fourteen RVs were identified and detetcted in 15/139 (10.8%) patients, and p.Ala4399Thr was detected in 17/139 (12.2%) patients. Hemispheres with GG and p.Ala4399Thr presented with significantly less ischemic events and more hemorrhagic events at diagnosis (p = 0.001 and p = 0.028, respectively). In asymptomatic hemispheres, those with GG were more susceptible to de novo hemorrhage than those with GA (adjusted hazard ratio [aHR] 5.36) with an increased risk when accompanied by p.Ala4399Thr or RVs (aHR 15.22 and 16.60, respectively). Within the choroidal anastomosis-positive hemispheres, GG exhibited a higher incidence of de novo hemorrhage than GA (p = 0.004). The GG of p. Arg4810Lys was a risk factor for de novo hemorrhage in asymptomatic MMD hemispheres. This risk increased with certain other variants and is observed in choroidal anastomosis-positive hemispheres. A comprehensive evaluation of RNF213 variants and angioarchitectures is essential for predicting the phenotype of asymptomatic hemispheres in MMD.
    Language English
    Publishing date 2023-06-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2541897-X
    ISSN 1868-601X ; 1868-4483
    ISSN (online) 1868-601X
    ISSN 1868-4483
    DOI 10.1007/s12975-023-01159-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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