CAD EYE Detection of Remaining Lesions After EMR

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

UNKNOWN

Clinical Phase

NA

Total Enrollment

60 participants

Study Classification

INTERVENTIONAL

Study Start Date

2022-09-12

Study Completion Date

2024-09-12

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Colorectal Dysplasia Colorectal Neoplasms

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

PARALLEL

Non-blinded, single center, non-randomized prospective pilot study

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Endoscopic mucosal resection + CAD-Eye™

This group constitutes patients with lesions suggestive of high-grade dysplasia or early invasive cancer approached with endoscopic mucosal resection, subjected to colonoscopy + CAD-Eye™ system evaluation for the detection of remaining malignant tissue.

For this group, the investigators used as a complement tool an AI system (CAD-Eye™) for the detection of remaining lesions immediately after EMR and in a three-month follow-up.

Group Type EXPERIMENTAL