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Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging

  • Author Footnotes
    ∗ Authors Takashi Kanesaka and Tsung-Chun Lee contributed equally to the article.
    Takashi Kanesaka
    Footnotes
    ∗ Authors Takashi Kanesaka and Tsung-Chun Lee contributed equally to the article.
    Affiliations
    Department of Gastrointestinal Oncology, Osaka International Cancer Institute (formerly Osaka Medical Center for Cancer and Cardiovascular Diseases), Osaka, Japan
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  • Author Footnotes
    ∗ Authors Takashi Kanesaka and Tsung-Chun Lee contributed equally to the article.
    Tsung-Chun Lee
    Footnotes
    ∗ Authors Takashi Kanesaka and Tsung-Chun Lee contributed equally to the article.
    Affiliations
    Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
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  • Noriya Uedo
    Correspondence
    Reprint requests: Noriya Uedo, MD, Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69, Otemae, Chuo-ku, Osaka 541-8567, Japan.
    Affiliations
    Department of Gastrointestinal Oncology, Osaka International Cancer Institute (formerly Osaka Medical Center for Cancer and Cardiovascular Diseases), Osaka, Japan
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  • Kun-Pei Lin
    Affiliations
    Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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  • Huai-Zhe Chen
    Affiliations
    Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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  • Ji-Yuh Lee
    Affiliations
    Department of Internal Medicine, National Taiwan University Hospital, Yunlin Branch, Yunlin, Taiwan
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  • Hsiu-Po Wang
    Correspondence
    Hsiu-Po Wang, MD, Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, No. 7, Chung-Shan South Road, Taipei 10002, Taiwan.
    Affiliations
    Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
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  • Hsuan-Ting Chang
    Correspondence
    Hsuan-Ting Chang, PhD, Photonics and Information Laboratory, Department of Electrical Engineering, National Yunlin University of Science and Technology, No. 123, Section 3, University Road, Douliu, Yunlin, 64002 Taiwan.
    Affiliations
    Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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  • Author Footnotes
    ∗ Authors Takashi Kanesaka and Tsung-Chun Lee contributed equally to the article.
Published:December 07, 2017DOI:https://doi.org/10.1016/j.gie.2017.11.029

      Background and Aims

      Magnifying narrow-band imaging (M-NBI) is important in the diagnosis of early gastric cancers (EGCs) but requires expertise to master. We developed a computer-aided diagnosis (CADx) system to assist endoscopists in identifying and delineating EGCs.

      Methods

      We retrospectively collected and randomly selected 66 EGC M-NBI images and 60 non-cancer M-NBI images into a training set and 61 EGC M-NBI images and 20 non-cancer M-NBI images into a test set. After preprocessing and partition, we determined 8 gray-level co-occurrence matrix (GLCM) features for each partitioned 40 × 40 pixel block and calculated a coefficient of variation of 8 GLCM feature vectors. We then trained a support vector machine (SVMLv1) based on variation vectors from the training set and examined in the test set. Furthermore, we collected 2 determined P and Q GLCM feature vectors from cancerous image blocks containing irregular microvessels from the training set, and we trained another SVM (SVMLv2) to delineate cancerous blocks, which were compared with expert-delineated areas for area concordance.

      Results

      The diagnostic performance revealed accuracy of 96.3%, precision (positive predictive value [PPV]) of 98.3%, recall (sensitivity) of 96.7%, and specificity of 95%, at a rate of 0.41 ± 0.01 seconds per image. The performance of area concordance, on a block basis, demonstrated accuracy of 73.8% ± 10.9%, precision (PPV) of 75.3% ± 20.9%, recall (sensitivity) of 65.5% ± 19.9%, and specificity of 80.8% ± 17.1%, at a rate of 0.49 ± 0.04 seconds per image.

      Conclusions

      This pilot study demonstrates that our CADx system has great potential in real-time diagnosis and delineation of EGCs in M-NBI images.

      Abbreviations:

      CADx (computer-aided diagnosis), EGC (early gastric cancer), ESD (endoscopic submucosal dissection), GLCM (gray-level co-occurrence matrix), M-NBI (magnifying narrow-band imaging), NBI (narrow-band imaging), PPV (positive predictive value), SVM (support vector machine), SVMLv1 (level-1 SVM), SVMLv2 (level-2 SVM)
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      Linked Article

      • Deep learning–based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective
        Gastrointestinal EndoscopyVol. 88Issue 1
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          We read with great interest the recent article in which Kanesaka et al1 reported a computer-aided system for identifying early gastric cancers (EGC). The diagnostic performance (accuracy of 96.3%) suggests the great potential of computer-aided diagnosis for EGC. This is especially true in countries such as China that have a high incidence of gastric cancer but a low EGC detection rate.2 Recent reports3 have estimated that about 679,100 new cases of gastric cancer were confirmed in China each year and that more than 80% of patients received their diagnoses at an advanced stage with poor prognosis.
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