Background and Aims
Diagnosing the invasion depth of gastric cancer (GC) is necessary to determine the
optimal method of treatment. Although the efficacy of evaluating macroscopic features
and EUS has been reported, there is a need for more accurate and objective methods.
The primary aim of this study was to test the efficacy of novel artificial intelligence
(AI) systems in predicting the invasion depth of GC.
Methods
A total of 16,557 images from 1084 cases of GC for which endoscopic resection or surgery
was performed between January 2013 and June 2019 were extracted. Cases were randomly
assigned to training and test datasets at a ratio of 4:1. Through transfer learning
leveraging a convolutional neural network architecture, ResNet50, 3 independent AI
systems were developed. Each system was trained to predict the invasion depth of GC
using conventional white-light imaging (WLI), nonmagnifying narrow-band imaging (NBI),
and indigo-carmine dye contrast imaging (Indigo).
Results
The area under the curve of the WLI AI system was .9590. The lesion-based sensitivity,
specificity, accuracy, positive predictive value, and negative predictive value of
the WLI AI system were 84.4%, 99.4%, 94.5%, 98.5%, and 92.9%, respectively. The lesion-based
accuracies of the WLI, NBI, and Indigo AI systems were 94.5%, 94.3%, and 95.5%, respectively,
with no significant difference.
Conclusions
These new AI systems trained with multiple images from different angles and distances
could predict the invasion depth of GC with high accuracy. The lesion-based accuracy
of the WLI, NBI, and Indigo AI systems was not significantly different.
Graphical abstract
Graphical Abstract
Abbreviations:
AI (artificial intelligence), GC (gastric cancer), Indigo (indigo-carmine dye contrast imaging), NBI (narrow-band imaging), SM2 (cancer with submucosal invasion ≥500 μm), WLI (white-light imaging)To read this article in full you will need to make a payment
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References
- Global cancer statistics, 2012.CA Cancer J Clin. 2015; 65: 87-108
- Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study.JAMA Oncol. 2019; : 51749-51768
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2018; 68: 394-424
- A scoring system to stratify curability after endoscopic submucosal dissection for early gastric cancer: "eCura system".Am J Gastroenterol. 2017; 112: 874-881
- Incidence of lymph node metastasis from early gastric cancer: estimation with a large number of cases at two large centers.Gastric Cancer. 2000; 3: 219-225
- Incidence of lymph node metastasis and the feasibility of endoscopic resection for undifferentiated-type early gastric cancer.Gastric Cancer. 2009; 12: 148-152
- A non-randomized confirmatory trial of an expanded indication for endoscopic submucosal dissection for intestinal-type gastric cancer (cT1a): the Japan Clinical Oncology Group study (JCOG0607).Gastric Cancer. 2018; 21: 114-123
- The effectiveness and safety of endoscopic submucosal dissection compared with endoscopic mucosal resection for early gastric cancer: a systematic review and metaanalysis.Surg Endosc. 2011; 25: 2666-2677
- A meta-analysis of endoscopic submucosal dissection and EMR for early gastric cancer.Gastrointest Endosc. 2012; 76: 763-770
- Long-term outcomes of endoscopic submucosal dissection for early gastric cancer: a retrospective comparison with conventional endoscopic resection in a single center.Gastric Cancer. 2014; 17: 130-136
- Japanese gastric cancer treatment guidelines 2014 (version 4).Gastric Cancer. 2017; 20: 1-19
- Endoscopic submucosal dissection: European Society of Gastrointestinal Endoscopy (ESGE) guideline.Endoscopy. 2015; 47: 829-854
- Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer.Endoscopy. 2010; 42: 705-713
- Integrated diagnostic strategy for the invasion depth of early gastric cancer by conventional endoscopy and EUS.Gastrointest Endosc. 2015; 82: 452-459
- Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.Gastric Cancer. 2018; 21: 653-660
- Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy.Gastroenterology. 2018; 155: 1069-1078
- Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy.Gastrointest Endosc. 2019; 89: 806-815
- A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer.J Clin Med. 2019; 8
- Japanese classification of gastric carcinoma: 3rd English edition.Gastric Cancer. 2011; 14: 101-112
- Update on the Paris classification of superficial neoplastic lesions in the digestive tract.Endoscopy. 2005; 37: 570-578
- Depth-predicting score for differentiated early gastric cancer.Gastric Cancer. 2011; 14: 35-40
- Diagnostic performance of conventional endoscopy in the identification of submucosal invasion by early gastric cancer: the "non-extension sign" as a simple diagnostic marker.Gastric Cancer. 2017; 20: 304-313
- Endoscopic prediction of tumor invasion depth in early gastric cancer.Gastrointest Endosc. 2011; 73: 917-927
- Clinical efficacy of endoscopic ultrasonography for decision of treatment strategy of gastric cancer.Surg Endosc. 2018; 32: 3789-3797
- Application of magnifying endoscopy with narrow-band imaging in diagnosing gastric lesions: a prospective study.Gastrointest Endosc. 2012; 76: 1124-1132
- Usefulness of magnifying endoscopy with narrow-band imaging for determining tumor invasion depth in early gastric cancer.Gastroenterol Res Pract. Epub 2013 Jan 17;
- Artificial intelligence-assisted polyp detection for colonoscopy: initial experience.Gastroenterology. 2018; 154: 2027-2029
- Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study.Gut. 2019; 68: 1813-1819
Article Info
Publication History
Published online: June 24, 2020
Accepted:
June 15,
2020
Received:
April 29,
2020
Footnotes
DISCLOSURE: The following author disclosed financial relationships: T. Tada: CEO and shareholder for AI Medical Service Inc. All other authors disclosed no financial relationships.
Identification
Copyright
© 2020 by the American Society for Gastrointestinal Endoscopy
