Clinical trial NCT05542030: CAD EYE Detection of Remaining Lesions After EMR
Official title: Accuracy of CAD Eye in the Detection of Colonic Remaining Lesions After Endoscopic Mucosal Resection: a Pilot Study
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Colorectal Dysplasia
Colorectal Neoplasms
Keywords: Artificial Intelligence
Colonoscopy
Endoscopic mucosal resection
Computer-assisted diagnosis
Lead sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Sponsor class: OTHER
First posted: 2022-09-15
Enrollment: 60
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Non-blinded, single center, non-randomized prospective pilot study
Primary purpose: DIAGNOSTIC

Brief summary: In the last decade, many innovative systems have been developed to support and improve the diagnosis accuracy during endoscopic studies. CAD-Eye™ (Fujifilm, Tokyo, Japan) is a computer-assisted diagnostic (CADx) system that uses artificial intelligence for the detection and characterization of polyps during colonoscopy. However, the accuracy of CAD-Eye™ in the recognition of remaining lesions after endoscopic mucosal resection (EMR) has not been broadly evaluated.

Finally, based on the importance of complete resection of the colonic mucosal lesions, namely suspicious high-grade dysplasia or early invasive cancer, the investigators aimed to assess the accuracy of CAD-Eye™ in the detection of remaining lesions after the procedure.

Detailed description: Nowadays, the increased polyp and adenoma detection rate, and its early treatment have reduced considerably colorectal cancer-related mortality. For lesions suspicious of high-grade dysplasia or early invasive cancer, the endoscopic mucosal resection (EMR), along with snare polypectomy, is now considered one of the established standard treatments. However, there are many ´difficult-to-treat lesions´ such as the large and fibrotic ones, which can lead to incomplete resections.

Based on the above, many newly diagnostic techniques guided by artificial intelligence (AI), currently proposed to improve the polyp detection rate during colonoscopy, can be applied for the detection of remaining lesions after endoscopic treatment.

CAD-Eye™ is CADx for polyp detection and characterization. It improves polyp visualization by using techniques such as blue-laser imaging (BLI-LASER), blue-light imaging (BLI-LED), and linked-color imaging (LCI). This device aimed to improve real-time polyp detection, helping experts identify multiple polyps simultaneously and common inadvertently missed lesions (flat lesions, polyps in difficult areas).

CAD-Eye™ had demonstrated in previous studies an accuracy of 89% to 91.7% in polyp detection. However, few studies had demonstrated its performance in the detection of remaining lesions after EMR. The investigators aimed to take advantage of this system in the detection of remaining lesions immediately after EMR and in its endoscopic control after three months.

Eligibility criteria: Inclusion Criteria:

* Patients referred to our center with an indication of colonoscopy and EMR for the treatment of lesions suspicious of high-grade dysplasia and early invasive cancer.
* Patients who authorize EMR and colonoscopy.
* Signed informed consent

Exclusion Criteria:

* Any clinical condition which makes EMR inviable.
* Poor bowel preparation score defined as the total Boston bowel preparation score (BBPS) \<6 and the right-segment score \<2
* Patients with more than one previous EMR
* Lost on a three-month follow-up after EMR
* Pregnancy or nursing

Primary outcomes:
measure: Lesions recurrence after EMR
timeFrame: up to 1 week
measure: Lesions recurrence in a three-month follow-up after EMR
timeFrame: up to 3 months
Secondary outcomes:
measure: Recurrence risk after EMR
timeFrame: up to 1 week
Other outcomes:
Locations:
city: Guayaquil
country: Ecuador
facility: Carlos Robles-Medranda
state: Guayas
Study officials:
affiliation: Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
name: Carlos Robles-Medranda, MD FASGE
role: PRINCIPAL_INVESTIGATOR
References:
citation: Kliegis L, Obst W, Bruns J, Weigt J. Can a Polyp Detection and Characterization System Predict Complete Resection? Dig Dis. 2022;40(1):115-118. doi: 10.1159/000516974. Epub 2021 May 6.
citation: Yoshida N, Inoue K, Tomita Y, Kobayashi R, Hashimoto H, Sugino S, Hirose R, Dohi O, Yasuda H, Morinaga Y, Inada Y, Murakami T, Zhu X, Itoh Y. An analysis about the function of a new artificial intelligence, CAD EYE with the lesion recognition and diagnosis for colorectal polyps in clinical practice. Int J Colorectal Dis. 2021 Oct;36(10):2237-2245. doi: 10.1007/s00384-021-04006-5. Epub 2021 Aug 18.
citation: Dumoulin FL, Hildenbrand R. Endoscopic resection techniques for colorectal neoplasia: Current developments. World J Gastroenterol. 2019 Jan 21;25(3):300-307. doi: 10.3748/wjg.v25.i3.300.
citation: Neumann H, Kreft A, Sivanathan V, Rahman F, Galle PR. Evaluation of novel LCI CAD EYE system for real time detection of colon polyps. PLoS One. 2021 Aug 26;16(8):e0255955. doi: 10.1371/journal.pone.0255955. eCollection 2021.
citation: Min M, Deng P, Zhang W, Sun X, Liu Y, Nong B. Comparison of linked color imaging and white-light colonoscopy for detection of colorectal polyps: a multicenter, randomized, crossover trial. Gastrointest Endosc. 2017 Oct;86(4):724-730. doi: 10.1016/j.gie.2017.02.035. Epub 2017 Mar 9.
citation: Tate DJ, Desomer L, Klein A, Brown G, Hourigan LF, Lee EY, Moss A, Ormonde D, Raftopoulos S, Singh R, Williams SJ, Zanati S, Byth K, Bourke MJ. Adenoma recurrence after piecemeal colonic EMR is predictable: the Sydney EMR recurrence tool. Gastrointest Endosc. 2017 Mar;85(3):647-656.e6. doi: 10.1016/j.gie.2016.11.027. Epub 2016 Nov 28.