Clinical trial NCT04673136: Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program.
Official title: Usefulness of GI-GENIUS in FIT-based Colorectal Cancer Screening Program. Randomized Controlled Trial.
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Colorectal Neoplasms
Intestinal Neoplasms
Gastrointestinal Neoplasms
Colonic Polyp
Colorectal Cancer
Adenoma Colon
Serrated Adenoma
Serrated Polyp
Lead sponsor: Asociación Española de Gastroenterología
Sponsor class: OTHER
Collaborators: name: Medtronic
First posted: 2020-12-17
Enrollment: 3400
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

Brief summary: 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Detection of advanced lesions
timeFrame: 15 months
Secondary outcomes:
measure: Detection of other type of lesions
timeFrame: 15 months
measure: Characteristics of GI-GENIUS
timeFrame: 1 month
Other outcomes:
Locations:
city: Alicante
country: Spain
facility: Hospital General Universitario de Alicante
city: Barcelona
country: Spain
facility: Hospital Clinic Barcelona
city: Ourense
country: Spain
facility: Complexo Hospitalario de Ourense
city: Oviedo
country: Spain
facility: Hospital Universitario Central de Asturias
city: Valladolid
country: Spain
facility: Hospital Universitario Río Hortega
city: Vigo
country: Spain
facility: Hospital Universitario Álvaro Cunqueiro
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.