Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis

Abstract Background Upper gastrointestinal cancers are aggressive malignancies with poor prognosis, even following multimodality therapy. As such, they require timely and accurate diagnostic and surveillance strategies; however, such radiological workflows necessitate considerable expertise and resource to maintain. In order to lessen the workload upon already stretched health systems, there has been increasing focus on the development and use of artificial intelligence (AI)-centred diagnostic systems. This systematic review summarizes the clinical applicability and diagnostic performance of AI-centred systems in the diagnosis and surveillance of esophagogastric cancers. Methods A systematic review was performed using the MEDLINE, EMBASE, Cochrane Review, and Scopus databases. Articles on the use of AI and radiomics for the diagnosis and surveillance of patients with esophageal cancer were evaluated, and quality assessment of studies was performed using the QUADAS-2 tool. A meta-analysis was performed to assess the diagnostic accuracy of sequencing methodologies. Results Thirty-six studies that described the use of AI were included in the qualitative synthesis and six studies involving 1352 patients were included in the quantitative analysis. Of these six studies, four studies assessed the utility of AI in gastric cancer diagnosis, one study assessed its utility for diagnosing esophageal cancer, and one study assessed its utility for surveillance. The pooled sensitivity and specificity were 73.4% (64.6–80.7) and 89.7% (82.7–94.1), respectively. Conclusions AI systems have shown promise in diagnosing and monitoring esophageal and gastric cancer, particularly when combined with existing diagnostic methods. Further work is needed to further develop systems of greater accuracy and greater consideration of the clinical workflows that they aim to integrate within.

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Diagnostic accuracy of circulating tumor cells detection in gastric cancer: systematic review and meta-analysis

BMC Cancer ◽

10.1186/1471-2407-13-314 ◽

2013 ◽

Vol 13 (1) ◽

Cited By ~ 19

Author(s):

Lanhua Tang ◽

Shushan Zhao ◽

Wei Liu ◽

Nicholas F Parchim ◽

Jin Huang ◽

...

Keyword(s):

Gastric Cancer ◽

Systematic Review ◽

Diagnostic Accuracy ◽

Tumor Cells ◽

Circulating Tumor Cells ◽

Meta Analysis

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Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis

F1000Research ◽

10.12688/f1000research.25548.1 ◽

2020 ◽

Vol 9 ◽

pp. 1086

Author(s):

Kamyab Keshtkar ◽

Abbas Keshtkar ◽

Alireza Safarpour

Keyword(s):

Neural Network ◽

Colorectal Cancer ◽

Systematic Review ◽

Convolutional Neural Network ◽

Prediction Models ◽

Meta Analysis ◽

Geographical Area ◽

Colorectal Polyps ◽

Primary Data ◽

Data Synthesis

Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Although colonoscopy screening has been proven as an effective strategy for preventing CRC unfortunately, even conventional colonoscopy by expert gastroenterologists can miss adenomas or pre-cancerous lesions in up to 25% of cases. This systematic review aimed to classify colorectal polyps (CRP) or CRC in endoscopic clinic settings using a new machine learning method, convolutional neural network (CNN). Methods: We will search PubMed/MEDLINE, Scopus, Web of Science, IEEE, Inspec, ProQuest, Google Scholar, Microsoft Academic Search, ScienceOpen, arXiv, and bioRxiv from 1st January 2010 to the 31th of July 2020. Our search will not be restricted based on language or geographical area. The primary studies will be selected that have observational design (cross-sectional, case control or cohort); the study subjects will be adult patients (>= 18 years old) referred to colonoscopy clinics; and the results of their colonoscopy evaluation will be available in the form of images or videos. The extracted data will be combined using meta-analysis of prediction models. The primary data synthesis will be performed based on area under curve-receiver operating characteristic curve and/or accuracy measures. We will use Stata version 14.2 (Statacorp; College Station, TX) for primary and secondary data synthesis. Conclusion: The inferences of our secondary research will provide evidence to evaluate the prognostic role of CNN in discriminating CRP or CRC in colonoscopy settings.

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