Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program.
NCT ID: NCT04673136
Last Updated: 2022-04-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
3400 participants
INTERVENTIONAL
2021-04-01
2022-03-31
Brief Summary
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Colonoscopies after a positive fecal immunochemical test (FIT) could be the scenario with a higher prevalence of advanced lesions which could be the ideal situation for demonstrating if these CADe systems are able also to increase the detection of advanced lesions and which kind of advanced lesions are these systems able to detect.
The CADILLAC study will randomize individuals within the population-based Spanish colorectal cancer screening program to receive a colonoscopy where the endoscopist is assisted by the GI-GENIUS device or to receive a standard colonoscopy.
If our results are positive, that could suppose a big step forward for CADe devices, in terms of definitive demonstration of being of help for efectively identify also advanced lesions.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
DIAGNOSTIC
NONE
Study Groups
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Colonoscopy assisted by GI-GENIUS
GI-GENIUS Medtronic
Colonoscopy assisted by GI-GENIUS device
Standard colonoscopy
Colonoscopy
Standard colonoscopy
Interventions
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GI-GENIUS Medtronic
Colonoscopy assisted by GI-GENIUS device
Colonoscopy
Standard colonoscopy
Eligibility Criteria
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Inclusion Criteria
* Complete colonoscopy with cecal intubation.
* Inform consent signed.
Exclusion Criteria
* Family history of colorectal cancer: ≥2 FDR or ≥1 FDR diagnosed before 50 years of age.
* Family history of hereditary colorectal cancer syndromes: Lynch syndrome, FAP, etc.
* Personal history of inflammatory bowel disease.
* Terminal illness.
* Personal history of total proctocolectomy.
18 Years
ALL
Yes
Sponsors
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Medtronic
INDUSTRY
Asociación Española de Gastroenterología
OTHER
Responsible Party
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Locations
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Hospital General Universitario de Alicante
Alicante, , Spain
Hospital Clinic Barcelona
Barcelona, , Spain
Complexo Hospitalario de Ourense
Ourense, , Spain
Hospital Universitario Central de Asturias
Oviedo, , Spain
Hospital Universitario Río Hortega
Valladolid, , Spain
Hospital Universitario Álvaro Cunqueiro
Vigo, , Spain
Countries
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References
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Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis. J Gastroenterol Hepatol. 2020 Oct;35(10):1676-1683. doi: 10.1111/jgh.15070. Epub 2020 Apr 26.
Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, Liu P, Li L, Song Y, Zhang D, Li Y, Xu G, Tu M, Liu X. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019 Oct;68(10):1813-1819. doi: 10.1136/gutjnl-2018-317500. Epub 2019 Feb 27.
Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, Lei L, Li L, Guo Z, Lei S, Xiong F, Wang H, Song Y, Pan Y, Zhou G. 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 Apr;5(4):343-351. doi: 10.1016/S2468-1253(19)30411-X. Epub 2020 Jan 22.
Mangas-Sanjuan C, de-Castro L, Cubiella J, Diez-Redondo P, Suarez A, Pellise M, Fernandez N, Zarraquinos S, Nunez-Rodriguez H, Alvarez-Garcia V, Ortiz O, Sala-Miquel N, Zapater P, Jover R; CADILLAC study investigators. Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias : A Randomized Trial. Ann Intern Med. 2023 Sep;176(9):1145-1152. doi: 10.7326/M22-2619. Epub 2023 Aug 29.
Other Identifiers
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CADILLAC-01
Identifier Type: -
Identifier Source: org_study_id
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