Artificial intelligence for non-polypoid colorectal neoplasms.
CADe
artificial intelligence
colonoscopy
detection
non-polypoid neoplasia
Journal
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
ISSN: 1443-1661
Titre abrégé: Dig Endosc
Pays: Australia
ID NLM: 9101419
Informations de publication
Date de publication:
Jan 2021
Jan 2021
Historique:
received:
10
07
2020
revised:
31
07
2020
accepted:
04
08
2020
pubmed:
9
8
2020
medline:
29
7
2021
entrez:
9
8
2020
Statut:
ppublish
Résumé
The miss rate of flat advanced colorectal neoplasia is still unacceptably high, especially in the Western setting, notwithstanding the widespread implementation of quality improvement programs and training. It is well known that flat morphology is associated with miss rate of colorectal neoplasia, and that this subset of lesions often shows a more aggressive biological behaviour. Artificial intelligence (AI) applied to the detection of colorectal neoplasia has been shown to increase adenoma detection rate, consistently across all lesion sizes and locations in the colon. However, there is still uncertainty whether AI can reduce the miss rate of flat advanced neoplasia, mainly because all published trials report a low number of flat colorectal lesions in their training sets, and this could reduce AI accuracy for this subset of lesions. In addition, flat lesions have different morphologies with variable prevalence and potentially different accuracy in their detection. For example, the subtle appearance and rarer frequency of a non-granular laterally spreading tumor (LST) could be much harder to identify than a granular mixed LST. In this review, we present a summary of the evidence on the role of AI in the identification of colorectal flat neoplasia, with a focus on data regarding presence of LSTs in the training/validation sets of the AI systems currently available on the market.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
285-289Informations de copyright
© 2020 Japan Gastroenterological Endoscopy Society.
Références
Byrne M. Artificial intelligence in gastroenterology. Tech Gastrointest Endosc 2019: 150641.
Ahmad OF, Stoyanov D, Lovat LB. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues.. Tech Gastrointest Endosc 2020; 22: 80-4.
Corley DA, Jensen CD, Marks AR et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med 2014; 370: 1298-306.
Kaminski MF, Regula J, Kraszewska E et al. Quality indicators for colonoscopy and the risk of interval cancer. N Engl J Med 2010; 362: 1795-803.
Kaminski MF, Thomas-Gibson S, Bugajski M et al. Performance measures for lower gastrointestinal endoscopy: A European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2017; 49: 378-97.
Rees CJ, Thomas Gibson S, Rutter MD et al. UK key performance indicators and quality assurance standards for colonoscopy. Gut 2016; 65: 1923-9.
Greenspan M, Rajan KB, Baig A et al. Advanced adenoma detection rate is independent of nonadvanced adenoma detection rate. Am J Gastroenterol 2013; 108: 1286-92.
Anderson JC, Rex DK, Robinson C et al. Association of small versus diminutive adenomas and the risk for metachronous advanced adenomas: Data from the New Hampshire Colonoscopy Registry. Gastrointest Endosc 2019; 90: 495-501.
Laish I, Sergeev I, Stein A et al. Risk of metachronous advanced lesions after resection of diminutive and small, non-advanced adenomas. Clin Res Hepatol Gastroenterol 2019; 43: 201-7.
Kim NH, Jung YS, Lee MY et al. Risk of developing metachronous advanced colorectal neoplasia after polypectomy in patients with multiple diminutive or small adenomas. Am J Gastroenterol 2019; 114: 1657-64.
Vleugels JLA, Hazewinkel Y, Fockens P et al. Natural history of diminutive and small colorectal polyps: A systematic literature review. Gastrointest Endosc 2017; 85: 1169-76.e1.
Zhao S, Wang S, Pan P et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: A systematic review and meta-analysis. Gastroenterology 2019; 156: 1661-74.e11.
Facciorusso A, Triantafyllou K, Murad MH et al. Compared abilities of endoscopic techniques to increase colon adenoma detection rates: A network meta-analysis. Clin Gastroenterol Hepatol 2019; 17: 2439-54.e25.
Abdeljawad K, Vemulapalli KC, Kahi CJ et al. Sessile serrated polyp prevalence determined by a colonoscopist with a high lesion detection rate and an experienced pathologist. Gastrointest Endosc 2015; 81: 517-24.
Russo P, Barbeiro S, Awadie H et al. Management of colorectal laterally spreading tumors: A systematic review and meta-analysis. Endosc Int Open 2019; 7: E239-59.
Pereyra L, Zamora R, Gómez EJ et al. Risk of metachronous advanced neoplastic lesions in patients with sporadic sessile serrated adenomas undergoing colonoscopic surveillance. Am J Gastroenterol 2016; 111: 871-8.
Erichsen R, Baron JA, Hamilton-Dutoit SJ et al. Increased risk of colorectal cancer development among patients with serrated polyps. Gastroenterology 2016; 150: 895-902.e5.
Holme Ø, Bretthauer M, Eide TJ et al. Long-term risk of colorectal cancer in individuals with serrated polyps. Gut 2015; 64: 929-36.
Kudo S, Lambert R, Allen JI et al. Nonpolypoid neoplastic lesions of the colorectal mucosa. Gastrointest Endosc 2008; 68: S3-47.
Participants in the Paris Workshop. The Paris endoscopic classification of superficial neoplastic lesions: Esophagus, stomach, and colon: November 30 to December 1, 2002. Gastrointest Endosc 2003; 58: S3-43.
Ohki D, Tsuji Y, Shinozaki T et al. Sessile serrated adenoma detection rate is correlated with adenoma detection rate. World J Gastrointest Oncol 2018; 10: 82-90.
Rodríguez de Santiago E, Hernanz N, Marcos-Prieto HM et al. Rate of missed oesophageal cancer at routine endoscopy and survival outcomes: A multicentric cohort study. United Eur Gastroenterol J 2019; 7: 189-98.
Bergman JJGHM, de Groof AJ, Pech O et al. An interactive web-based educational tool improves detection and delineation of Barrett's esophagus-related neoplasia. Gastroenterology 2019; 156: 1299-308.e3.
Zhou G, Xiao X, Tu M et al. Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy. PLoS One 2020; 15: e0231880.
Hassan C, Spadaccini M, Iannone A et al. Performance of artificial intelligence for colonoscopy regarding adenoma and polyp detection: A meta-analysis. Gastrointest Endosc Published online: 26 Jun 2020; https://doi.org/10.1016/j.gie.2020.06.059
Mori Y, Kudo S, Misawa M et al. Artificial intelligence for colorectal polyp detection and characterization. Curr Treat Options Gastroenterol 2020; 18: 200-11.
Urban G, Tripathi P, Alkayali T et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018; 155: 1069-78.e8.
Misawa M, Kudo S-E, Mori Y et al. Artificial intelligence-assisted polyp detection for colonoscopy: Initial experience. Gastroenterology 2018; 154: 2027-9.e3.
Wang P, Berzin TM, Glissen Brown JR et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study. Gut 2019; 68: 1813-9.
Yamada M, Saito Y, Imaoka H et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep 2019; 9: 14465.
Hassan C, Wallace MB, Sharma P et al. New artificial intelligence system: First validation study versus experienced endoscopists for colorectal polyp detection. Gut 2020; 69: 799-800.
Seibt H, Beyer A, Häfner M et al. Evaluation of a real-time artificial intelligence system using a deep neural network for polyp detection and localization in the lower gastrointestinal tract. In: Endoscopy. © Georg Thieme Verlag KG 2020. OP58.
Weigt J. Standalone performance of a new CADe/CADx for detection and characterization of colorectal neoplasia. Submitt Publ 2020.
Wang P, Liu X, Berzin TM et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): A double-blind randomised study. Lancet Gastroenterol Hepatol 2020; 5: 343-51.
Hassan C, Badalamenti M, Maselli R et al. Computer-aided detection-assisted colonoscopy: Classification and relevance of false positives. Gastrointest Endosc 2020; 92: 900-4.e4