scholarly journals Current Evidence and Future Perspective of Accuracy of Artificial Intelligence Application for Early Gastric Cancer Diagnosis With Endoscopy: A Systematic and Meta-Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiang Kailin ◽  
Jiang Xiaotao ◽  
Pan Jinglin ◽  
Wen Yi ◽  
Huang Yuanchen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Publication Bias ◽  
Hot Spot ◽  
Diagnostic Value ◽  
Future Perspective ◽  
Current Evidence ◽  
Cochrane Library ◽  
Funnel Plot

Background & Aims: Gastric cancer is the common malignancies from cancer worldwide. Endoscopy is currently the most effective method to detect early gastric cancer (EGC). However, endoscopy is not infallible and EGC can be missed during endoscopy. Artificial intelligence (AI)-assisted endoscopic diagnosis is a recent hot spot of research. We aimed to quantify the diagnostic value of AI-assisted endoscopy in diagnosing EGC.Method: The PubMed, MEDLINE, Embase and the Cochrane Library Databases were searched for articles on AI-assisted endoscopy application in EGC diagnosis. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated, and the endoscopists' diagnostic value was evaluated for comparison. The subgroup was set according to endoscopy modality, and number of training images. A funnel plot was delineated to estimate the publication bias.Result: 16 studies were included in this study. We indicated that the application of AI in endoscopic detection of EGC achieved an AUC of 0.96 (95% CI, 0.94–0.97), a sensitivity of 86% (95% CI, 77–92%), and a specificity of 93% (95% CI, 89–96%). In AI-assisted EGC depth diagnosis, the AUC was 0.82(95% CI, 0.78–0.85), and the pooled sensitivity and specificity was 0.72(95% CI, 0.58–0.82) and 0.79(95% CI, 0.56–0.92). The funnel plot showed no publication bias.Conclusion: The AI applications for EGC diagnosis seemed to be more accurate than the endoscopists. AI assisted EGC diagnosis was more accurate than experts. More prospective studies are needed to make AI-aided EGC diagnosis universal in clinical practice.

scholarly journals Diagnostic value of macrophage inhibitory cytokine 1 as a novel prognostic biomarkers for early gastric cancer screening

2020 ◽  
Author(s):  
Xin Ge ◽  
Xiaolei Zhang ◽  
Yanling Ma ◽  
Shaohua Chen ◽  
Zhaowu Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Cancer Diagnosis ◽  
Early Stage ◽  
Precancerous Lesions ◽  
Diagnostic Value ◽  
Low Grade ◽  
Serum Samples ◽  
Early Screening ◽  
Roc Curve Analysis

Abstract BACKGROUND Early diagnosis is very important to improve the survival rate of patients with gastric cancer, especially in asymptomatic participants. However, low sensitivity of common biomarkers has caused difficulties in early screening of gastric cancer. In this study, we explored whether MIC-1 can improve the detection rate of early gastric cancer.METHODS We screened 8,257 participants based on risk factors such as age, gender, and family history for physical examination including gastroscopy. Participant blood samples were taken for measure MIC-1, CA-199, CA72-4 and PG1/PG2 levels. The diagnostic performance of MIC-1 was assessed and compared with CA-199, CA72-4 and PG1/PG2, and its role in early gastric cancer diagnosis and the assessment of the risk of precancerous lesions have also been studied.RESULTS Based on endoscopic and histopathological findings, 55 participants had gastric cancer, 566 participants had low-grade neoplasia, 2605 participants had chronic gastritis. MIC-1 levels were significantly elevated in gastric cancer serum samples as compared to controls (p<0.001). The sensitivity of serum MIC-1 for gastric cancer diagnosis was much higher than that of CA-199 (49.1% vs. 20.0%) with similar specificities. Moreover, receiver operating characteristic (ROC) curve analysis also showed that serum MIC-1 had a better performance compared with CA-199, CA72-4 and PG1/PG2 in distinguishing early-stage gastric cancer (AUC: 72.9% vs. 69.5%, 67.5%, 44.0% respectively).CONCLUSIONS Serum MIC-1 is significantly elevated in most patients with early gastric cancer. MIC-1 can serve as a novel diagnostic marker of early gastric cancer and value the risk of gastric cancer.

scholarly journals Factors Affecting the Accuracy of Endoscopic Ultrasonography in the Diagnosis of Early Gastric Cancer Invasion Depth: A Meta-analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Ding Shi ◽  
Xiao-xia Xi
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Likelihood Ratio ◽  
Endoscopic Ultrasonography ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Diagnostic Value ◽  
Depth Of Invasion ◽  
Invasion Depth ◽  
Sensitivity Specificity

Background. Endoscopic ultrasonography (EUS) is the first imaging modality for investigating the depth of invasion in early gastric cancer (EGC). However, there is presently no consensus on the accuracy of EUS in diagnosing the invasion depth of EGC. Aim. This study is aimed at systematically evaluating the accuracy of EUS in diagnosing the invasion depth of EGC and its affecting factors. Methods. The literatures were identified by searching PubMed, SpringerLink, Cochrane Library, Web of Science, Nature, and Karger knowledge databases. Two researchers extracted the data from the literature and reconstructed these in 2×2 tables. The Meta-DiSc software was used to evaluate the overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic advantage ratio, and 95% confidence interval (CI). The SROC was drawn, and the area under the curve (AUC) was calculated to evaluate the diagnostic value. Results. A total of 17 articles were selected, which included 4525 cases of lesions. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic dominance ratio, and 95% CI of EUS for diagnosing EGC was 0.87 (95% CI: 0.86-0.88), 0.67 (95% CI: 0.65-0.70), 2.90 (95% CI: 2.25-3.75), 0.17 (95% CI: 0.13-0.23), and 18.25 (95% CI: 12.61-26.39), respectively. The overall overstaging rate of mucosa/submucosa 1 (M/SM1) and SM by EUS was 13.31% and 32.8%, respectively, while the overall understaging rate of SM was 29.7%. The total misdiagnosis rates for EUS were as follows: 30.4% for lesions≥2 cm and 20.9% for lesions<2 cm, 27.7% for ulcerative lesions and 21.4% for nonulcerative lesions, and 22% for differentiated lesions and 26.9% for undifferentiated lesions. Conclusion. EUS has a moderate diagnostic value for the depth of invasion of EGC. The shape, size, and differentiation of lesions might be the main factors that affect the accuracy of EUS in diagnosing EGC.

scholarly journals Sa1235 APPLICATION OF ARTIFICIAL INTELLIGENCE FOR PREDICTION OF INVASION DEPTH IN EARLY GASTRIC CANCER: PRELIMINARY STUDY

2018 ◽  
Vol 87 (6) ◽  
pp. AB176 ◽  
Author(s):  
Hong Jin Yoon ◽  
Seunghyup Kim ◽  
Jie-Hyun Kim ◽  
Ji-Soo Keum ◽  
Junik Jo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Invasion Depth ◽  
Preliminary Study

scholarly journals Association between lncRNA GAS5, MEG3, and PCAT-1 Polymorphisms and Cancer Risk: A Meta-Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyan Dong ◽  
Wenyan Gao ◽  
Xiaoling LV ◽  
Yazhen Wang ◽  
Qing Wu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Risk ◽  
Publication Bias ◽  
Fixed Effects ◽  
Cancer Susceptibility ◽  
Meta Analysis ◽  
Biomedical Literature ◽  
Cochrane Library ◽  
Gastric Cancer Risk ◽  
Lncrna Gas5

Purpose. Long noncoding RNAs (lncRNAs) have been widely studied, and single nucleotide polymorphisms (SNPs) in lncRNAs are considered to be genetic factors that influence cancer susceptibility. The lncRNA GAS5, MEG3, and PCAT-1 polymorphisms are shown to be possibly associated with cancer risk. The aim of this meta-analysis was to systematically evaluate this association. Methods. Studies were selected from PubMed, Web of Science, Embase, Google Scholar, Cochrane Library, the Chinese National Knowledge Infrastructure (CNKI), and the Chinese Biomedical Literature Database (CBM) through inclusion and exclusion criteria. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using the random-effects model or fixed-effects model to assess the association between lncRNA polymorphisms and cancer susceptibility. Metaregression and publication bias analyses were also conducted. All analyses were performed using the Stata 12.0 software. Results. Sixteen articles (covering 13750 cases and 17194 controls) were included in this meta-analysis. A significant association between SNP rs145204276 and gastric cancer risk was observed (del vs. ins: OR=0.79, 95%CI=0.72‐0.86; del/del vs. ins/ins+del/ins: OR=0.74, 95%CI=0.59‐0.91; del/ins vs. ins/ins: OR=0.84, 95%CI=0.67‐1.05). For rs16901904, a decreased cancer risk was observed in three genetic models (C vs. T: OR=0.79, 95%CI=0.70‐0.90; CC vs. CT+TT: OR=0.49, 95%CI=0.37‐0.65; CC vs. TT: OR=0.49, 95%CI=0.37‐0.66). No statistical significance was found in the metaregression analysis. For all of the included SNPs, no publication bias was found in all genotype models. Conclusions. The rs145204276 SNP in lncRNA GAS5 is likely to be associated with gastric cancer risk, whereas the rs16901904 SNP in lncRNA PCAT-1 bears association with a decreased cancer risk.

scholarly journals Artificial intelligence for early gastric cancer: early promise and the path ahead

2019 ◽  
Vol 89 (4) ◽  
pp. 816-817 ◽  
Author(s):  
Yuichi Mori ◽  
Tyler M. Berzin ◽  
Shin-ei Kudo
Keyword(s):  
Artificial Intelligence ◽  
Gastric Cancer ◽  
Early Gastric Cancer
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