scholarly journals Contour analysis of endoscopic images in the diagnosis of early gastric cancer

2020 ◽  
pp. 53-56
Author(s):  
K. S. Prikhodko ◽  
N. N. Mitrakova ◽  
A. A. Rozhentsov ◽  
A. A. Mitrakov
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Narrow Band Imaging ◽  
Objective Assessment ◽  
Digital Processing ◽  
Malignant Neoplasms ◽  
Contour Analysis ◽  
Endoscopic Images ◽  
Diagnostic Signs ◽  
Normal Mucous Membrane

The purpose of the study: The main purpose of the study is creation of a system for digital processing of endoscopic images in white light and narrow band imaging for the early diagnosis of early forms of stomach cancer.Materials and methods: The object of the study is endoscopic images (6320 cases) with PENTAX EG-2790K and OLYMPUS H180 devices. The subjects of the research are mathematical models of gastric epithelial neoplasias, classifi cation of these tissues, methods of digital image processing and contour analysis, methods of mathematical modeling.Results: The work is divided into two stages to obtain quantitative estimates of the studied characteristics: 1. To make a diagnostic map — the image is segmented, then the boundaries between the pathologically altered tissues and the normal mucous membrane are drawn. 2. The calculation of the characteristics of the contour associated with diagnostic signs is made. As a measure of the symmetry of the figure, the symmetry coefficient k was used, defined as the ratio of the number of samples of the normalized autocorrelation function that exceeded the specified threshold in level to the total number of samples. The study revealed that the contours of malignant neoplasms have a symmetry coefficient k < 0,05, and the contours of benign neoplasms k > 0,2. This suggests the possibility of automated differentiation of neoplasms based on the analysis of their shape.The conclusion: An objective assessment of endoscopic signs of early gastric cancer is necessary to standardize and systematize the diagnostic approach. The unified digital processing of endoscopic images will allow the endoscopist to increase the frequency of detecting early forms of gastric cancer, which will affect to reduce mortality.

Endoscopic submucosal dissection by using Clutch cutter and IT knife 2 for early gastric cancer with severe submucosal fibrosis: a case report

MedPharmRes ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 17-21
Author(s):  
Nhan Le ◽  
Phuong Vien ◽  
Nghia Le
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Endoscopic Submucosal Dissection ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Magnifying Endoscopy ◽  
Gastrointestinal Cancers ◽  
Pathological Result ◽  
Well Differentiated ◽  
Submucosal Fibrosis

Introduction: Gastric cancer is one of the highly malignant gastrointestinal cancers and the third leading cause of cancer death. In the last decade, early gastric cancer (EGC) has been reported by using narrow-band imaging (NBI) magnifying endoscopy. Advances in endoscopic techniques, such as endoscopic submucosal dissection (ESD), have enabled the en bloc resection of these EGC. Although ESD is performed for early gastric cancer, there are still many difficult problems in technique of this procedure. The difficulty of gastric ESD depends on the size and location of a tumor, presence of severe submucosal fibrosis, presence of ulceration... We report a case of our successful ESD by using Clutch cutter and IT knife 2 in treatment of EGC with severe submucosal fibrosis. Case presentation: A 62-year-old man felt an epigastric discomfort two months ago. The narrow-band imaging (NBI) magnifying endoscopy revealed a suspected early gastric cancer type 0 - IIa + IIc (Japanese classification of early gastrointestinal cancers) at the incisura angularis, the size of this lesion was 15 mm in diameter, and pathological result of endoscopic biopsy was a well-differentiated adenocarcinoma. ESD was performed and we found there was severe submucosal fibrosis which was dissected safer and faster by using Clutch cutter and IT knife 2. There were no complications such as severe bleeding and perforation. The size of resected specimen was 60 x 35 mm and the time of procedure was 150 minutes. After ESD, the pathological result was a well differentiated adenocarcinoma, pT1a, UL(-), LY(-), V(-), no cancer cell in vertical and horizontal margins. The healing time of ESD-induced ulcer was 5 weeks without local recurrence. Conclusion: Through this case, we aim to emphasize the importance of using Clutch cutter and IT knife 2 as a modified technique which makes ESD a safe procedure in treatment of EGC with severe submucosal fibrosis.

Magnifying Blue Laser Imaging versus Magnifying Narrow-Band Imaging for the Diagnosis of Early Gastric Cancer: A Prospective, Multicenter, Comparative Study

Digestion ◽  
2017 ◽  
Vol 96 (3) ◽  
pp. 127-134 ◽  
Author(s):  
Osamu Dohi ◽  
Nobuaki Yagi ◽  
Shigeto Yoshida ◽  
Shoko Ono ◽  
Yoji Sanomura ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Comparative Study ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Blue Laser ◽  
Laser Imaging ◽  
Blue Laser Imaging

scholarly journals Diagnostic limitations of magnifying endoscopy with narrow-band imaging in early gastric cancer

2020 ◽  
Vol 08 (10) ◽  
pp. E1233-E1242
Author(s):  
Kohei Matsumoto ◽  
Hiroya Ueyama ◽  
Takashi Yao ◽  
Daiki Abe ◽  
Shotaro Oki ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Gastric Adenocarcinoma ◽  
Narrow Band ◽  
Narrow Band Imaging ◽  
Magnifying Endoscopy ◽  
Clinicopathological Features ◽  
Fundic Gland ◽  
Undifferentiated Adenocarcinoma ◽  
Histological Characteristics

Abstract Background and study aims Magnifying endoscopy with narrow band imaging (M-NBI) has made a huge contribution to endoscopic diagnosis of early gastric cancer (EGC). However, we sometimes encountered false-negative cases with M-NBI diagnosis (i. e., M-NBI diagnostic limitation lesion: M-NBI-DLL). However, clinicopathological features of M-NBI-DLLs have not been well elucidated. We aimed to clarify the clinicopathological features and histological reasons of M-NBI-DLLs. Patients and methods In this single-center retrospective study, M-NBI-DLLs were extracted from 456 EGCs resected endoscopically at our hospital. We defined histological types of M-NBI-DLLs and analyzed clinicopathologically to clarify histological reasons of M-NBI-DLLs. Results Of 456 EGCs, 48 lesions (10.5 %) of M-NBI-DLLs were enrolled. M-NBI-DLLs was classified into four histological types as follows: gastric adenocarcinoma of fundic-gland type (GA-FG, n = 25), gastric adenocarcinoma of fundic-gland mucosal type (GA-FGM, n = 1), differentiated adenocarcinoma (n = 14), and undifferentiated adenocarcinoma (n = 8). Thirty-nine lesions of M-NBI-DLLs were H. pylori-negative gastric cancers (39/47, 82.9 %). Histological reasons for M-NBI-DLLs were as follows: 1) completely covered with non-neoplastic mucosa (25/25 GA-FG, 8/8 undifferentiated adenocarcinoma); 2) well-differentiated adenocarcinoma with low-grade atypia (1/1 GA-FGM, 14/14 differentiated adenocarcinoma); 3) similarity of surface structure (10/14 differentiated adenocarcinoma); and 4) partially covered and/or mixed with a non-neoplastic mucosa (1/1 GA-FGM, 6/14 differentiated adenocarcinoma). Conclusions Diagnostic limitations of M-NBI depend on four distinct histological characteristics. For accurate diagnosis of M-NBI-DLLs, it may be necessary to fully understand endoscopic features of these lesions using white light imaging and M-NBI based on these histological characteristics and to take a precise biopsy.

scholarly journals A Lesion-Based Convolutional Neural Network Improves Endoscopic Detection and Depth Prediction of Early Gastric Cancer

2019 ◽  
Vol 8 (9) ◽  
pp. 1310 ◽  
Author(s):  
Hong Jin Yoon ◽  
Seunghyup Kim ◽  
Jie-Hyun Kim ◽  
Ji-Soo Keum ◽  
Sang-Il Oh ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Treatment Method ◽  
Factors Affecting ◽  
Receiver Operating Characteristic Curves ◽  
Endoscopic Images ◽  
Depth Prediction ◽  
Undifferentiated Type ◽  
Histologic Differentiation

In early gastric cancer (EGC), tumor invasion depth is an important factor for determining the treatment method. However, as endoscopic ultrasonography has limitations when measuring the exact depth in a clinical setting as endoscopists often depend on gross findings and personal experience. The present study aimed to develop a model optimized for EGC detection and depth prediction, and we investigated factors affecting artificial intelligence (AI) diagnosis. We employed a visual geometry group(VGG)-16 model for the classification of endoscopic images as EGC (T1a or T1b) or non-EGC. To induce the model to activate EGC regions during training, we proposed a novel loss function that simultaneously measured classification and localization errors. We experimented with 11,539 endoscopic images (896 T1a-EGC, 809 T1b-EGC, and 9834 non-EGC). The areas under the curves of receiver operating characteristic curves for EGC detection and depth prediction were 0.981 and 0.851, respectively. Among the factors affecting AI prediction of tumor depth, only histologic differentiation was significantly associated, where undifferentiated-type histology exhibited a lower AI accuracy. Thus, the lesion-based model is an appropriate training method for AI in EGC. However, further improvements and validation are required, especially for undifferentiated-type histology.

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