Background and Aims
Although narrow-band imaging (NBI) is a useful modality for detecting and delineating
esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining
the margins of some lesions even with NBI. This study aimed to develop an artificial
intelligence (AI) system for detecting superficial ESCC and precancerous lesions and
delineating the extent of lesions under NBI.
Methods
Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and
external image test datasets were used to evaluate the detection and delineation performance
of the system. The delineation performance of the system was compared with that of
endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment,
and its real-time diagnostic capability was prospectively estimated.
Results
The system was trained and tested using 10,047 still images and 140 videos from 1112
patients and 1183 lesions. In the image testing, the accuracy of the system in detecting
lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy
of the system in delineating extents in internal and external tests was 88.9% and
87.0%, respectively. The delineation performance of the system was superior to that
of junior endoscopists and similar to that of senior endoscopists. In the prospective
clinical evaluation, the system exhibited satisfactory performance, with an accuracy
of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents.
Conclusions
The proposed AI system could accurately detect superficial ESCC and precancerous lesions
and delineate the extent of lesions under NBI.
Abbreviations:
AI (artificial intelligence), ER (endoscopic resection), ESCC (esophageal squamous cell carcinoma), FN (false negative), FP (false positive), mIoU (mean intersection over union), NBI (narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), TP (true positive), WCHSCU (West China Hospital of Sichuan University)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Gastrointestinal EndoscopyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2021; 71: 209-249
- Epidemiology of esophageal squamous cell carcinoma.Gastroenterology. 2018; 154: 360-373
- Genomic comparison of esophageal squamous cell carcinoma and its precursor lesions by multi-region whole-exome sequencing.Nat Commun. 2017; 8: 524
- Endoscopic management of early adenocarcinoma and squamous cell carcinoma of the esophagus: screening, diagnosis, and therapy.Gastroenterology. 2018; 154: 421-436
- Endoscopic submucosal dissection/endoscopic mucosal resection guidelines for esophageal cancer.Dig Endosc. 2020; 32: 452-493
- Endoscopic submucosal dissection for superficial gastrointestinal lesions: European Society of Gastrointestinal Endoscopy (ESGE) guideline—update 2022.Endoscopy. 2022; 54: 591-622
- Narrow-band imaging versus Lugol chromoendoscopy for esophageal squamous cell cancer screening in normal endoscopic practice: randomized controlled trial.Endoscopy. 2021; 53: 674-682
- Delineating the extent of esophageal squamous cell carcinoma.Esophagus. 2021; 18: 790-796
- Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos).Gastrointest Endosc. 2020; 91: 41-51
- Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging.World J Gastroenterol. 2021; 27: 281-293
- Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation.Dig Endosc. 2021; 33: 1101-1109
- Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: a multicenter study.J Gastroenterol Hepatol. 2022; 37: 169-178
- Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.Gastrointest Endosc. 2019; 90: 407-414
- Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.Esophagus. 2020; 17: 250-256
- Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence.J Gastroenterol. 2020; 55: 1037-1045
- Endoscopic images by a Single-Shot Multibox Detector for the identification of early cancerous lesions in the esophagus: a pilot study.Cancers. 2021; 13: 321
- Use of a convolutional neural network for classifying microvessels of superficial esophageal squamous cell carcinomas.J Gastroenterol Hepatol. 2021; 36: 2239-2246
- A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: comparing diagnostic performance with a panel of expert European and Asian endoscopists.Gastrointest Endosc. 2021; 94: 273-281
- Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study.Surg Endosc. 2022; 36: 8651-8662
- Artificial intelligence for detecting and delineating margins of early ESCC under WLI endoscopy.Clin Transl Gastroenterol. 2022; 13e00433
- YOLACT++ better real-time instance segmentation.IEEE Trans Pattern Anal Mach Intell. 2022; 44: 1108-1121
- Screening for oesophageal cancer.Nat Rev Clin Oncol. 2012; 9: 278-287
- Esophageal ESD: technique and prevention of complications.Gastrointest Endosc Clin North Am. 2014; 24: 201-212
- Usefulness of non-magnifying narrow-band imaging in screening of early esophageal squamous cell carcinoma: a prospective comparative study using propensity score matching.Am J Gastroenterol. 2014; 109: 845-854
- Narrow band imaging versus Lugol chromoendoscopy to diagnose squamous cell carcinoma of the esophagus: a systematic review and meta-analysis.BMC Cancer. 2017; 17: 54
- Narrow-band imaging provides reliable screening for esophageal malignancy in patients with head and neck cancers.Am J Gastroenterol. 2009; 104: 2942-2948
- Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.Gastrointest Endosc. 2019; 89: 25-32
- Endoscopic detection and differentiation of esophageal lesions using a deep neural network.Gastrointest Endosc. 2020; 91: 301-309
- Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video).Gastrointest Endosc. 2020; 92: 848-855
- Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.Sci Rep. 2021; 11: 7759
Article info
Publication history
Published online: December 09, 2022
Accepted:
December 1,
2022
Received:
July 31,
2022
Footnotes
DISCLOSURE: All authors disclosed no financial relationships. Research support for this study (B. Hu) was provided by the National Natural Science Foundation of China (grant no. 82170675) and 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (grant no. ZYJC21011).
Identification
Copyright
© 2023 by the American Society for Gastrointestinal Endoscopy
