Takashi Kanesaka1, Tsung-Chun Lee2, Noriya Uedo1, Kun-Pei Lin3, Huai-Zhe Chen3, Ji-Yuh Lee4, Hsiu-Po Wang2, Hsuan-Ting Chang3. 1. Department of Gastrointestinal Oncology, Osaka International Cancer Institute (formerly Osaka Medical Center for Cancer and Cardiovascular Diseases), Osaka, Japan. 2. Department of Internal Medicine, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan. 3. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan. 4. Department of Internal Medicine, National Taiwan University Hospital, Yunlin Branch, Yunlin, Taiwan.
Abstract
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.
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.
Authors: Fei Kuang; Juan Du; Mengjia Zhou; Xiangdong Liu; Xinchen Luo; Yong Tang; Bo Li; Song Su Journal: Front Oncol Date: 2022-06-10 Impact factor: 5.738
Authors: Gerard Cummins; Benjamin F Cox; Gastone Ciuti; Thineskrishna Anbarasan; Marc P Y Desmulliez; Sandy Cochran; Robert Steele; John N Plevris; Anastasios Koulaouzidis Journal: Nat Rev Gastroenterol Hepatol Date: 2019-07 Impact factor: 46.802