• Title/Summary/Keyword: Research on gastric cancer

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Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

  • Kyung Won Kim;Jimi Huh ;Bushra Urooj ;Jeongjin Lee ;Jinseok Lee ;In-Seob Lee ;Hyesun Park ;Seongwon Na ;Yousun Ko
    • Journal of Gastric Cancer
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    • v.23 no.3
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    • pp.388-399
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    • 2023
  • Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

Effect of a Proton Pump Inhibitor on Tumor Bleeding Prevention in Unresectable Gastric Cancer Patients: a Double-Blind, Randomized, Placebo-Controlled Trial

  • Kim, Young-Il;Kim, Mi-Jung;Park, Sook Ryun;Kim, Hark Kyun;Cho, Soo-Jeong;Lee, Jong Yeul;Kim, Chan Gyoo;Kim, Gwang Ha;Park, Moo In;Nam, Byung-Ho;Park, Young Iee;Choi, Il Ju
    • Journal of Gastric Cancer
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    • v.17 no.2
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    • pp.120-131
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    • 2017
  • Purpose: Tumor bleeding is a major complication in inoperable gastric cancer. The study aim was to investigate the effects of proton pump inhibitor (PPI) treatment for the prevention of gastric tumor bleeding. Materials and Methods: This study was a prospective double-blind, randomized, placebo-controlled trial. Patients with inoperable gastric cancer were randomly assigned to receive oral lansoprazole (30 mg) or placebo daily. The primary endpoint was the occurrence of tumor bleeding, and the secondary endpoints were transfusion requirement and overall survival (OS). Results: This study initially planned to enroll 394 patients, but prematurely ended due to low recruitment rate. Overall, 127 patients were included in the analyses: 64 in the lansoprazole group and 63 in the placebo group. During the median follow-up of 6.4 months, tumor bleeding rates were 7.8% and 9.5%, in the lansoprazole and placebo groups, respectively, with the cumulative bleeding incidence not statistically different between the groups (P=0.515, Gray's test). However, during the initial 4 months, 4 placebo-treated patients developed tumor bleeding, whereas there were no bleeding events in the lansoprazole-treated patients (P=0.041, Gray's test). There was no difference in the proportion of patients who required transfusion between the groups. The OS between the lansoprazole (11.7 months) and the placebo (11.0 months) groups was not statistically different (P=0.610). Study drug-related serious adverse event or bleeding-related death did not occur. Conclusions: Treating patients with inoperable gastric cancer with lansoprazole did not significantly reduce the incidence of tumor bleeding. However, further studies are needed to evaluate whether lansoprazole can prevent tumor bleeding during earlier phases of chemotherapy (ClinicalTrial.gov, identifier No. NCT02150447).

Gastric Adenocarcinoma of Fundic Gland Type with Aggressive Transformation and Lymph Node Metastasis: a Case Report

  • Okumura, Yasuhiro;Takamatsu, Manabu;Ohashi, Manabu;Yamamoto, Yorimasa;Yamamoto, Noriko;Kawachi, Hiroshi;Ida, Satoshi;Kumagai, Koshi;Nunobe, Souya;Hiki, Naoki;Sano, Takeshi
    • Journal of Gastric Cancer
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    • v.18 no.4
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    • pp.409-416
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    • 2018
  • A 55-year-old man visited our hospital for a detailed examination of a gastric submucosal tumor that was first detected 10 years prior. The tumor continued to grow and had developed a depressed area in its center. A histopathological examination of biopsy specimens revealed gastric adenocarcinoma of the fundic gland type (GA-FG). It was diagnosed as T2 based on the invasion depth as determined by white-light endoscopy and endoscopic ultrasonography. A total gastrectomy with lymphadenectomy was performed and a GA-FG in the mucosa and submucosa was confirmed histopathologically. However, there was a gradual transition to an infiltrative tubular adenocarcinoma with poorly differentiated components in the muscular and subserosal layers. Metastasis was identified in a dissected lymph node (LN). This is the first report of a GA-FG progressing to an aggressive cancer with LN metastasis. These findings modify our understanding of the pathophysiology of GA-FG.

Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

  • Takahiro Kinoshita;Masaru Komatsu
    • Journal of Gastric Cancer
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    • v.23 no.3
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    • pp.400-409
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    • 2023
  • Artificial intelligence (AI) has made significant progress in recent years, and many medical fields are attempting to introduce AI technology into clinical practice. Currently, much research is being conducted to evaluate that AI can be incorporated into surgical procedures to make them safer and more efficient, subsequently to obtain better outcomes for patients. In this paper, we review basic AI research regarding surgery and discuss the potential for implementing AI technology in gastric cancer surgery. At present, research and development is focused on AI technologies that assist the surgeon's understandings and judgment during surgery, such as anatomical navigation. AI systems are also being developed to recognize in which the surgical phase is ongoing. Such a surgical phase recognition systems is considered for effective storage of surgical videos and education, in the future, for use in systems to objectively evaluate the skill of surgeons. At this time, it is not considered practical to let AI make intraoperative decisions or move forceps automatically from an ethical standpoint, too. At present, AI research on surgery has various limitations, and it is desirable to develop practical systems that will truly benefit clinical practice in the future.

Significance of Thrombocytosis in Clinicopathologic Characteristics and Prognosis of Gastric Cancer

  • Li, Fang-Xuan;Wei, Li-Juan;Zhang, Huan;Li, Shi-Xia;Liu, Jun-Tian
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.16
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    • pp.6511-6517
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    • 2014
  • Purpose: We aimed to study the relationship between thrombocytosis and clinical features of gastric cancerfocussing on platelet counts and gastric cancer progression through different TNM stages. Methods: According to the normal range of platelet count in our institution, 1,596 patients were divided to two groups: a thrombocytosis group (120 patients, > $400{\times}1000/{\mu}L$) and a control group (1,476 patients, ${\leq}400{\times}1000/{\mu}L$). Results: The incidence of thrombocytosis was 7.5%. Higher platelet counts were observed in patients with older age, larger tumor size, deeper invasion, lymph node metastasis, distant metastasis and advanced TNM stage. In multivariate logistic regression, tumor size, depth of tumor invasion, lymph node metastasis and TNM stage were independent risk factors for thrombocytosis of gastric cancer patients. On prognostic analysis, age, tumor size, tumor location, histologic type, depth of tumor invasion, lymph node metastasis, distant metastasis and TNM stage and platelet count were important factors. Tumor size, invasion depth, lymph node metastasis, TNM stage and the platelet count were independent prognostic factors. Conclusion: Thrombocytosis is associated with clinical features of gastric cancer patients and correlates with a poor prognosis.

Manganese Superoxide Dismutase (MnSOD Val-9Ala) Gene Polymorphism and Susceptibility to Gastric Cancer

  • Moradi, Mohammad-Taher;Yari, Kheirollah;Rahimi, Zohreh;Kazemi, Elham;Shahbazi, Mehrdad
    • Asian Pacific Journal of Cancer Prevention
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    • v.16 no.2
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    • pp.485-488
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    • 2015
  • Background: Oxidative stress caused by the generation of reactive oxygen species plays an important role in human carcinogenesis. Manganese superoxide dismutase (MnSOD) Val-9Ala in the mitochondrial target sequence is the best known polymorphism of this enzyme. The purpose of the current research was to assess the association of MnSOD Val-9Ala genotypes with the risk of gastric cancer. Materials and Methods: This case-control study covered 54 gastric cancer patients compared to 100 cancer free subjects as controls. Extraction of DNA was performed on bioptic samples and genotypes were identified with a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results: The frequencies of MnSOD Ala/Ala, Ala/Val and Val/Val genotypes in healthy individuals were 24.3, 66.7 and 9%, respectively. However, in gastric cancer patients, Ala/Ala, Ala/Val and Val/Val were observed in 24.0, 48.0 and 28.0% (p=0.01). In patients the frequency of MnSOD Val allele was higher (52%) compared to that in controls (42%). Conclusions: The results of this study show a positive association between MnSOD Val-9Ala gene polymorphism and risk of gastric cancer disease in Iranian population.

Cost-effectiveness Outcomes of the National Gastric Cancer Screening Program in South Korea

  • Cho, Eun;Kang, Moon Hae;Choi, Kui Son;Suh, MiNa;Jun, Jae Kwan;Park, Eun-Cheol
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.4
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    • pp.2533-2540
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    • 2013
  • Background: Although screening is necessary where gastric cancer is particularly common in Asia, the performance outcomes of mass screening programs have remained unclear. This study was conducted to evaluate cost-effectiveness outcomes of the national cancer screening program (NCSP) for gastric cancer in South Korea. Materials and Methods: People aged 40 years or over during 2002-2003 (baseline) were the target population. Screening recipients and patients diagnosed with gastric cancers were identified using the NCSP and Korea Central Cancer Registry databases. Clinical outcomes were measured in terms of mortality and life-years saved (LYS) of gastric cancer patients during 7 years based on merged data from the Korean National Health Insurance Corporation and National Statistical Office. We considered direct, indirect, and productivity-loss costs associated with screening attendance. Incremental cost-effectiveness ratio (ICER) estimates were produced according to screening method, sex, and age group compared to non-screening. Results: The age-adjusted ICER for survival was 260,201,000-371,011,000 Korean Won (KW; 1USD=1,088 KW) for the upper-gastrointestinal (UGI) tract over non-screening. Endoscopy ICERs were lower (119,099,000-178,700,000 KW/survival) than UGI. To increase 1 life-year, additional costs of approximately 14,466,000-15,014,000 KW and 8,817,000-9,755,000 KW were required for UGI and endoscopy, respectively. Endoscopy was the most cost-effective strategy for males and females. With regard to sensitivity analyses varying based on the upper age limit, endoscopy NCSP was dominant for both males and females. For males, an upper limit of age 75 or 80 years could be considered. ICER estimates for LYS indicate that the gastric cancer screening program in Korea is cost-effective. Conclusion: Endoscopy should be recommended as a first-line method in Korea because it is beneficial among the Korean population.

Various Classification of Gastric Adenocarcinoma

  • Moon, Hee Seok;Jeong, Hyun Yong
    • Journal of Digestive Cancer Research
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    • v.7 no.1
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    • pp.8-12
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    • 2019
  • Despite its declining incidence, gastric cancer is globally, still, the third most common cause of cancer-related mortality. Gastric cancer is a heterogeneous disease with diverse pathogenesis and molecular backgrounds. Therefore several systems have been proposed to aid in the classification of gastric adenocarcinoma based on the macroscopic, microscopic and anatomical features of the tumor. However, these classifications did not reflect the pathogenesis of the disease. Recently, genomic analysis has identified several subtypes of gastric adenocarcinoma and a detailed understanding of the molecular biology behind the neoplastic phenotype is possible to develop of more effective therapies. We will describe the existing various classification of gastric cancer and the recently introduced molecular biology and immunological classification.

Phase II Study on Safety and Efficacy of Yadanzi® (Javanica oil emulsion injection) Combined with Chemotherapy for Patients with Gastric Cancer

  • Liu, Jin;Huang, Xin-En;Tian, Guang-Yu;Cao, Jie;Lu, Yan-Yan;Wu, Xue-Yan;Xiang, Jin
    • Asian Pacific Journal of Cancer Prevention
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    • v.14 no.3
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    • pp.2009-2012
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    • 2013
  • Objective: To investigate the efficacy and safety of Yadanzi$^{(R)}$ (Javanica oil emulsion injection) combined with chemotherapy for treatment of patients with advanced gastric cancer. Methods: From January 2011 to December 2012, we recruited 75 patients with advanced gastric cancer, who received javanica oil emulsion injection together with chemotherapy. After two cycles of treatment, efficacy and safety of the combined therapies were evaluated. Results: Overall response rate of 75 patients after treatment was 85.3% (CR+PR+SD). Treatment related side effects were recorded. No treatment related death occurred. Conclusions: Javanica oil emulsion injection combined with chemotherapy could be considered as a safe and effective regimen in treating patients with advanced gastric cancer. Further randomized clinical trials should be conducted to confirm whether the addition of Yadanzi$^{(R)}$ to chemotheraphy could be associated with reduced toxicity, enhanced tolerability and improved quality of life for patients with advanced gastric cancer.

Convolution Neural Network Based Auto Classification Model Using Endoscopic Images of Gastric Cancer and Gastric Ulcer (내시경의 위암과 위궤양 영상을 이용한 합성곱 신경망 기반의 자동 분류 모델)

  • Park, Ye Rang;Kim, Young Jae;Chung, Jun-Won;Kim, Kwang Gi
    • Journal of Biomedical Engineering Research
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    • v.41 no.2
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    • pp.101-106
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    • 2020
  • Although benign gastric ulcers do not develop into gastric cancer, they are similar to early gastric cancer and difficult to distinguish. This may lead to misconsider early gastric cancer as gastric ulcer while diagnosing. Since gastric cancer does not have any special symptoms until discovered, it is important to detect gastric ulcers by early gastroscopy to prevent the gastric cancer. Therefore, we developed a Convolution Neural Network (CNN) model that can be helpful for endoscopy. 3,015 images of gastroscopy of patients undergoing endoscopy at Gachon University Gil Hospital were used in this study. Using ResNet-50, three models were developed to classify normal and gastric ulcers, normal and gastric cancer, and gastric ulcer and gastric cancer. We applied the data augmentation technique to increase the number of training data and examined the effect on accuracy by varying the multiples. The accuracy of each model with the highest performance are as follows. The accuracy of normal and gastric ulcer classification model was 95.11% when the data were increased 15 times, the accuracy of normal and gastric cancer classification model was 98.28% when 15 times increased likewise, and 5 times increased data in gastric ulcer and gastric cancer classification model yielded 87.89%. We will collect additional specific shape of gastric ulcer and cancer data and will apply various image processing techniques for visual enhancement. Models that classify normal and lesion, which showed relatively high accuracy, will be re-learned through optimal parameter search.