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

• Published in: Gastrointestinal endoscopy Vol. 87; no. 5; pp. 1339 - 1344
• Main Authors: Kanesaka, Takashi, Lee, Tsung-Chun, Uedo, Noriya, Lin, Kun-Pei, Chen, Huai-Zhe, Lee, Ji-Yuh, Wang, Hsiu-Po, Chang, Hsuan-Ting
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2018
• Subjects: EGC; NBI; ESD; M-NBI; PPV; SVM; SVMLv2; CADx; GLCM; SVMLv1
• ISSN: 0016-5107; 1097-6779; 1097-6779
• DOI: 10.1016/j.gie.2017.11.029

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