• Journal of Gastric Cancer
• /
• v.12 no.1
• /
• pp.43-45
• /
• 2012

Mesenteric pseudocyst is rare. This term is used to describe the abdominal cystic mass, without the origin of abdominal organ. We presented a case of mesenteric pseudocyst of the small bowel in a 70-year-old man. Esophago-gastro-duodenoscopy showed a 3.5 cm sized excavated lesion on the posterior wall of angle. Endocopic biopsy confirmed a histologic diagnosis of the poorly differentiated adenocarcinoma, which includes the signet ring cell component. Abdominal computed tomography scan showed a focal mucosal enhancement in the posterior wall of angle of the stomach, a 2.4 cm sized enhancing mass on the distal small bowel loop, without distant metastases or ascites in rectal shelf, and multiple gallbladder stones. The patient underwent subtotal gastrectomy with gastroduodenostomy, segmental resection of the small bowel, and cholecystectomy. The final pathological diagnosis was mesenteric pseudocyst. This is the first case report describing incidentally detected mesenteric pseudocyst of the small bowel in gastric cancer patients.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.3
• /
• pp.19-28
• /
• 2021

Esophagogastroduodenoscopy is a method commonly used for early diagnosis of upper gastrointestinal lesions. However, 10-20 percent of the gastric lesions are reported to be missed, due to human error. And countries including the US, the UK, and Japan, the World Endoscopy Organization (WEO) suggested guidelines about essential gastrointestinal parts to take pictures of so that all gastric lesions are observed. In this paper, we propose deep learning techniques for classification of anatomical sites, aiming for the system that informs practitioners whether they successfully did the gastroscopy without blind spots. The proposed model uses pre-processing modules and data augmentation techniques suitable for gastroscopy images. Not only does the experiment result with a maximum F1 score of 99.6%, but it also shows a error rate of less than 4% based on the actual data. Given the performance results, we found the model to be explainable with the potential to be utilized in the clinical area.