https://www.ncbi.nlm.nih.gov/pubmed/29225083
Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging.
Author information
- 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.
Copyright © 2018. Published by Elsevier Inc.
Comment in
- Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective. [Gastrointest Endosc. 2018]
- Response. [Gastrointest Endosc. 2018]
- PMID:
- 29225083
- DOI:
- 10.1016/j.gie.2017.11.029
일본 오사카, 대만.
내시경 조기위암 확대 NBI 이미지 66개. 정상 소견 60개. 파일럿 스터디
8단계 레벨의 흑백 매트릭스 features , 40 x 40 픽셀 블럭 (8 gray-level co-occurrence matrix (GLCM) features for each partitioned 40 × 40 pixel block )
SVM 방법
정확도 96.3% ..
