Trial Title:
CAD EYE Detection of Remaining Lesions After EMR
NCT ID:
NCT05542030
Condition:
Colorectal Dysplasia
Colorectal Neoplasms
Conditions: Official terms:
Colorectal Neoplasms
Conditions: Keywords:
Artificial Intelligence
Colonoscopy
Endoscopic mucosal resection
Computer-assisted diagnosis
Study type:
Interventional
Study phase:
N/A
Overall status:
Recruiting
Study design:
Allocation:
Non-Randomized
Intervention model:
Parallel Assignment
Intervention model description:
Non-blinded, single center, non-randomized prospective pilot study
Primary purpose:
Diagnostic
Masking:
None (Open Label)
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
EMR with CAD-Eye™
Description:
Patients of group 1 undergoing Intervention 1 are subjected to an EMR with CAD-Eye™ to
detect the remaining lesions immediately after the endoscopic procedure.
The suspected remaining lesions in the post-procedure defect detected with CAD-Eye™ are
removed and sent to pathology to confirm the diagnosis.
Arm group label:
Endoscopic mucosal resection + CAD-Eye™
Intervention type:
Diagnostic Test
Intervention name:
EMR without CAD-Eye™
Description:
Patients of group 2, undergoing intervention 2, subjected to an EMR alone. The immediate
detection of remaining lesions is based on the visual impression of the expert.
The suspected remaining lesions in the post-procedure defect are removed and sent to
pathology to confirm the diagnosis.
Arm group label:
Endoscopic mucosal resection without CAD Eye
Intervention type:
Diagnostic Test
Intervention name:
Follow-up colonoscopy with CAD-Eye™
Description:
Patients undergoing Interventions 1 and 2, with a previous EMR, are assigned for a
three-month follow-up using the CAD-Eye™ as a complementary procedure to detect remaining
lesions.
For the detection of residual lesions, the colonoscope with the CAD-Eye™ assistance is
used during the post-procedural scar evaluation. Suspicious lesions detected are removed
and sent to pathology for final diagnosis.
Arm group label:
Endoscopic mucosal resection + CAD-Eye™
Arm group label:
Endoscopic mucosal resection without CAD Eye
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.
Criteria for 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
Gender:
All
Minimum age:
18 Years
Maximum age:
90 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Carlos Robles-Medranda
Address:
City:
Guayaquil
Zip:
090505
Country:
Ecuador
Status:
Recruiting
Contact:
Last name:
Carlos Robles-Medranda, MD FASGE
Phone:
+59342109180
Email:
carlosoakm@yahoo.es
Investigator:
Last name:
Hannah P. Lukashok, MD
Email:
Sub-Investigator
Investigator:
Last name:
Juan Alcivar-Vasquez, MD
Email:
Sub-Investigator
Investigator:
Last name:
Miguel Puga-Tejada, MD
Email:
Sub-Investigator
Investigator:
Last name:
Maria Egas-Izquierdo, MD
Email:
Sub-Investigator
Investigator:
Last name:
Jorge Baquerizo-Burgos, MD
Email:
Sub-Investigator
Investigator:
Last name:
Martha Arevalo-Mora, MD
Email:
Sub-Investigator
Investigator:
Last name:
Domenica Cunto, MD
Email:
Sub-Investigator
Start date:
September 12, 2022
Completion date:
September 12, 2024
Lead sponsor:
Agency:
Instituto Ecuatoriano de Enfermedades Digestivas
Agency class:
Other
Source:
Instituto Ecuatoriano de Enfermedades Digestivas
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05542030
