Artificial Intelligence for Diminutive Polyp Characterization

NCT05391477 | Unknown DMC

• 1 other identifier: Oddity
• Study Type: interventional
• Target: 643
• Locations: 1 country
• Sites: 1

Brief Summary

Artificial intelligence is a promising tool that may have a role in characterizing colon epithelial lesions (CADx), helping to get a reliable optical diagnosis regardless of the endoscopist experience. Performances of the different CADx systems are variable but it seems that, in most cases, high accuracy and sensitivities are achieved. However, these CADx systems have been developed and validated using still pictures or videos, and a real-world accurate test is lacking. No clinical trials have tested this technology in clinical practice and, therefore, performance in real colonoscopies, practical problems, applicability, and cost are unknown.

Conditions

Colorectal Neoplasms

Keywords

Artificial Intelligence; Colorectal cancer; Polyp; Characterization

Outcome Measures

Primary Outcomes (2)

• Comparison of the AIOD and HOD accuracy of the post-polypectomy surveillance interval assignment with respect to the surveillance interval assigned by pathology

  A surveillance interval will be assigned using optical diagnosis of ≤ 5 mm polyps (Arm 1: AIOD; Arm 2: HOD of polyps diagnosed with high confidence) plus histopathology of \> 5 mm polyps and polyps ≤ 5 mm diagnosed with low confidence. For each patient included, the optical-diagnosis surveillance assignment will be matched with the histology-directed one, and a concordance rate will be calculated. The post-polypectomy surveillance interval will be calculated using the ESGE 2020 and the USMSTF 2020 guidelines. Per-patient analysis.

  At the end of the study (2 years)

• Comparison of the AIOD and HOD negative predictive value (NPV) for adenoma in rectosigmoid polyps ≤ 5 mm with respect to histology

  The optical diagnosis of ≤ 5 mm rectosigmoid polyps (Arm 1: AIOD; Arm 2: HOD, only high-confidence diagnosis) reliability on ruling out the presence of an adenoma will be calculated using histopathology as the gold standard. Per-lesion analysis. NPV = number of confirmed hyperplastic polyps/number of hyperplastic optical diagnosis

  At the end of the study (2 years)

Secondary Outcomes (4)

• Comparison of the AIOD and HOD diagnostic accuracy parameters of polyps ≤ 5 mm (Arm 1: AIOD; Arm 2: HOD) with respect to histology

  Interim analysis (when half of the sample size had been included). At the end of the study (2 years)

• Cost-effectiveness of AIOD

  At the end of the study (2 years)

• Comparison of the proportion of adverse events in colonoscopies with and without the AIOD device.

  30 days after the colonoscopy (Day 30)

• Proportion of patients accepting to have their polyps diagnosed by the AI system or human optical diagnosis (designed questionnaire)

  Day of colonoscopy (Day 1)

Study Arms (2)

Human optical diagnosis (HOD)

NO INTERVENTION

The examinator will provide a HOD for every lesion (regardless of their size) found during the examination (adenoma vs non-adenoma) following one of the available validated classifications (NICE, JNET, BASIC). He/she will also give a level of confidence in his/her diagnosis (high/low confidence). However, only diminutive lesions will be considered when analyzing the main outcome. The time to get a HOD will be recorded. An in situ surveillance interval will be provided if possible.

Artificial intelligence optical diagnosis (AIOD):

EXPERIMENTAL

GI-Genius will provide an artificial intelligence diagnosis (AIOD) for every lesion detected (adenoma vs non-adenoma). Only diminutive lesions will be considered for the analysis of the main outcome. However, data on larger lesions will be recorded to describe GI-Genius´ performance in detail (secondary outcome). The time to get an AIOD will be recorded. An in situ surveillance interval will be provided if possible

Device: GI-Genius artificial intelligence

Interventions

GI-Genius artificial intelligence DEVICE

The software allows for the real-time characterization of framed polyps during a colonoscopy classifying them on adenoma or non-adenoma.

Artificial intelligence optical diagnosis (AIOD):

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients attending a colonoscopy within a population-based CRC screening program (FIT- or colonoscopy-based) or because of post-polypectomy surveillance,
• Written informed consent before the colonoscopy,

You may not qualify if:

• None, patient included
• Previous history of inflammatory bowel disease.
• Previous history of CRC
• Previous CR resection
• Polyposis or hereditary CRC syndrome
• Coagulopathy/Anticoagulants
• Unwillingness to participate

Sponsors & Collaborators

• Hospital Universitario La Fe: lead
• European Society of Gastrointestinal Endoscopy: collaborator
• Medtronic: collaborator

Study Sites (1)

Hospital Universitari i Politècnic La Fe

Valencia, 46026, Spain

RECRUITING

MeSH Terms

Conditions

Colorectal Neoplasms; Polyps

Condition Hierarchy (Ancestors)

Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Officials

• Marco Bustamante Balén, M.D., Ph.D.

  Hospital Universitario La Fe

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Marco Bustamante Balén, M.D., Ph.D.

CONTACT

+34 961244000; bustamante_mar@gva.es

Sylwia Jaworska Fernandez

CONTACT

9621244262; sylwia_jaworska@iislafe.es

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: Patients will be blinded to group allocation. The endoscopist in group 2 will be blinded to the AIOD. However, the endoscopist in group 1 will not be blinded to the CADx diagnosis because the output helps the endoscopist to focus the lesion properly for a AIOD diagnosis. In group 2, the person in charge of handling the GI-Genius output will communicate with the endoscopist when the AIOD of a particular lesion has been obtained. There is no need to mask personnel who enters HOD or AIOD data on the CRD because at that point results of pathology are not available.
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Model Details: The ODDITY trial is a European multicenter randomized, parallel-group superiority trial comparing GI-Genius artificial intelligence optical diagnosis (AIOD) to human optical diagnosis (HOD) of colon lesions ≤ 5 mm performed by endoscopists, using histopathology as the gold standard. A total of 643 patients attending a colonoscopy within a CRC screening program (either FIT- or colonoscopy-based) or because of post-polypectomy surveillance will be randomized to the ADI group (group 1) or the HOD (control, group 2) group. A computer-generated 1:1 blocking randomization scheme stratified for center and endoscopist will be used.

Study Record Dates

• First Submitted: April 5, 2022
• First Posted: May 26, 2022
• Study Start: February 27, 2023
• Primary Completion: May 1, 2024
• Study Completion: December 1, 2024
• Last Updated: May 31, 2023

Record last verified: 2023-05

Locations

ES (1)