CAD EYE Detection of Remaining Lesions After EMR
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
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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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: 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. |
Diagnostic Test: EMR with CAD-Eye™
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.
Diagnostic Test: Follow-up colonoscopy with CAD-Eye™
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.
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| Active Comparator: Endoscopic mucosal resection without CAD Eye This group constitutes patients with lesions suggestive of high-grade dysplasia or early invasive cancer approached with endoscopic mucosal resection and subjected to colonoscopy. The detection of remaining lesions immediately after EMR is based on the visual impression of the expert. For this group, the investigators used as a complement tool an AI system (CAD-Eye™) only for the evaluation of the post-procedure scar to detect remaining lesions in the three-month follow-up. |
Diagnostic Test: EMR without CAD-Eye™
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.
Diagnostic Test: Follow-up colonoscopy with CAD-Eye™
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.
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Outcome Measures
Primary Outcome Measures
- Lesions recurrence after EMR [up to 1 week]
Detection of remaining lesions immediately after EMR procedure based on endoscopist expertise (EMR without CAD-Eye™ group) or CAD-Eye™ (EMR + CAD-Eye™ group). Lesions will be confirmed by biopsy. Data will be summarized as frequencies.
- Lesions recurrence in a three-month follow-up after EMR [up to 3 months]
Evaluation of CAD-Eye™ in the detection of recurrent lesions after EMR procedure. Remaining lesions detected by CAD-Eye™ in the three-month follow-up. Lesions will be confirmed by biopsy. Data will be summarized as frequencies.
Secondary Outcome Measures
- Recurrence risk after EMR [up to 1 week]
Calculate de recurrence risk by the Sydney EMR recurrence tool (SERT) in a scale from 0 to 4 2 points: size of 40 mm or larger 1 point: Intraprocedural bleeding (IPB) 1 point: high-grade dysplasia (HGD) in histopathology
Eligibility Criteria
Criteria
Inclusion Criteria:
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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.
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Patients who authorize EMR and colonoscopy.
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Signed informed consent
Exclusion Criteria:
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Any clinical condition which makes EMR inviable.
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Poor bowel preparation score defined as the total Boston bowel preparation score (BBPS) <6 and the right-segment score <2
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Patients with more than one previous EMR
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Lost on a three-month follow-up after EMR
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Pregnancy or nursing
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Carlos Robles-Medranda | Guayaquil | Guayas | Ecuador | 090505 |
Sponsors and Collaborators
- Instituto Ecuatoriano de Enfermedades Digestivas
Investigators
- Principal Investigator: Carlos Robles-Medranda, MD FASGE, Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
Study Documents (Full-Text)
None provided.More Information
Publications
- 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. Review.
- 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.
- 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.
- 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.
- 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.
- 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.
- IECED-08202022