Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program.

NCT ID: NCT04673136

Last Updated: 2022-04-08

Study Results

Results pending

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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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

3400 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-04-01

Study Completion Date

2022-03-31

Brief Summary

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Deep learning technology has an increasing role in medical image applications and, recently, an artificial intelligence device has been developed and commercialized by Medtronic for identification of polyps during colonoscopy (GI-GENIUS). This kind of computer-aided detection (CADe) devices have demonstrated its ability for improving polyp detection rate (PDR) and the adenoma detection rate (ADR). However, this increase in PDR and ADR is mainly made at the expense of small polyps and non advanced adenomas.

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.

Detailed Description

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Conditions

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Colorectal Neoplasms Intestinal Neoplasms Gastrointestinal Neoplasms Colonic Polyp Colorectal Cancer Adenoma Colon Serrated Adenoma Serrated Polyp

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Colonoscopy assisted by GI-GENIUS

Group Type EXPERIMENTAL

GI-GENIUS Medtronic

Intervention Type DEVICE

Colonoscopy assisted by GI-GENIUS device

Standard colonoscopy

Group Type PLACEBO_COMPARATOR

Colonoscopy

Intervention Type OTHER

Standard colonoscopy

Interventions

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GI-GENIUS Medtronic

Colonoscopy assisted by GI-GENIUS device

Intervention Type DEVICE

Colonoscopy

Standard colonoscopy

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Individuals with a positive result in fecal immunochemical test within the population-based colorectal cancer screening program.
* Complete colonoscopy with cecal intubation.
* Inform consent signed.

Exclusion Criteria

* Personal history of colorectal cancer.
* 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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Medtronic

INDUSTRY

Sponsor Role collaborator

Asociación Española de Gastroenterología

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Hospital General Universitario de Alicante

Alicante, , Spain

Site Status

Hospital Clinic Barcelona

Barcelona, , Spain

Site Status

Complexo Hospitalario de Ourense

Ourense, , Spain

Site Status

Hospital Universitario Central de Asturias

Oviedo, , Spain

Site Status

Hospital Universitario Río Hortega

Valladolid, , Spain

Site Status

Hospital Universitario Álvaro Cunqueiro

Vigo, , Spain

Site Status

Countries

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Spain

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.

Reference Type RESULT
PMID: 32267558 (View on PubMed)

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.

Reference Type RESULT
PMID: 29928897 (View on PubMed)

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.

Reference Type RESULT
PMID: 30814121 (View on PubMed)

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.

Reference Type RESULT
PMID: 31981517 (View on PubMed)

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.

Reference Type DERIVED
PMID: 37639723 (View on PubMed)

Other Identifiers

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CADILLAC-01

Identifier Type: -

Identifier Source: org_study_id

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