Colon CancerNCT04260321
The AID Study 2: Artificial Intelligence for Colorectal Adenoma Detection 2
An observational study of Colon Cancer, run by Istituto Clinico Humanitas at 5 sites in 2 countries.
Trial summary
Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. Read more about this study
- Who is it for?
- Ages 40 to 80 years
- Conditions the study is for
- Colon Cancer
- What is being tested?
- AI
- Type of treatment
- Medical device
- How many people
- 700 participants
- Where
- 5 sitesin 2 countries
- Study dates
- 19 Feb 2020to 31 Dec 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Based on the observed prevalence of adenomas (35%) among patients undergoing colonoscopies at our center within the last 12 months, a sample size of 322 subjects per arm could allow for a 90% power to show the non-inferiority (primary end-point) of the AI-aided arm by excluding that the one-side 95% CI will exclude a difference of 10% in favour of the standard group. Such sample size will also have a 80% power to detect as statistical significant (α=0.05; two-sided test) a 10% absolute increase in the detection rate of adenomas in the AI-aided arm (secondary end-point). Performing a sub-stratification according to operator experience, we will planning to enroll the 322 subjects per arm performed by experts and 322 subjects per arm performed by average operator.
Who may be able to join
- All 40-80 years-old subjects undergoing a colonoscopy
Who cannot join
- subjects with personal history of CRC, or IBD.
- patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale > 2 in any colonic segment).
- patients with previous colonic resection.
- patients on antithrombotic therapy, precluding polyp resection.
- patients who were not able or refused to give informed written consent.
Trial locations and contacts
5 sites in 2 countries.
5 sites in 2 countries
Italy4 sites
Fondazione Poliambulanza
Brescia, Italia
Ospedale Valduce
Como, 22100
Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital
Rome, 00153
Endoscopy Unit, Humanitas Research Hospital
Rozzano, Milano, 20089
Switzerland1 site
Ente Ospedaliero Cantonale, Ospedale Italiano
Lugano, 6900
About this study
Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC.
Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.
Average experienced endoscopists (each having performed <2000 screening colonoscopies) will perform the endoscopic procedure.
- Conditions
- Colon Cancer
- Being tested
- AI
- Keywords
- Artificial Intelligence
- Medical subject headings
- Colonic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AIDevice
Artificial intelligence colonoscopy
| Study group | Role | What they receive |
|---|---|---|
| AI | Artificial intelligence colonoscopy Receives: Device: AI | |
| Control | White light colonoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Non-inferiority of AI-aided colonoscopy in terms of ADR
The proportion of participants with at least one adenoma (per-patient analysis).
Measured: 5 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Feb 2020
- Study start19 Feb 2020
- Primary completion31 Dec 2020
- Study completion31 Dec 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colon CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 700 participants (actual)
Sponsor and investigators
- Lead sponsor
- Istituto Clinico Humanitas
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Alessandro Repici, MD, Principal investigator, Humanitas Research Hospital IRCCS, Rozzano-Milan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04260321
- Sponsor's protocol ID
- 2363-2
- Status last verified
- Dec 2020
- First submitted
- 30 Jan 2020
- Last updated
- 5 Feb 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Feb 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The AID Study 2: Artificial Intelligence for Colorectal Adenoma Detection 2
- ClinicalTrials.gov ID
- NCT04260321
- Acronym / sponsor ID
- 2363-2
- Conditions
- Colon Cancer
- Keywords
- Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 700 participants (actual)
- Lead sponsor
- Istituto Clinico HumanitasUniversity, hospital or other
- Study start
- 19 Feb 2020 (actual)
- Primary completion
- 31 Dec 2020 (actual)
- Study completion
- 31 Dec 2020 (actual)
- First posted
- 7 Feb 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Colonoscopy is clinically used as the gold standard for detection of colon cancer (CRC) and removal of adenomatous polyps. Despite the success of colonoscopy in reducing cancer-related deaths, there exists a disappointing level of adenomas missed at colonoscopy. "Back-to-back" colonoscopies have indicated significant miss rates of 27% for small adenomas (< 5 mm) and 6% for adenomas of more than 10 mm in diameter. Studies performing both CT colonography and colonoscopy estimate that the colonoscopy miss rate for polyps over 10 mm in size may be as high as 12%. The clinical importance of missed lesions should be emphasized because these lesions may ultimately progress to CRC.
Limitations in human visual perception and other human biases such as fatigue, distraction, level of alertness during examination increases such recognition errors and way of mitigating them may be the key to improve polyp detection and further reduction in mortality from CRC. In the past years, a number of CAD systems for detection of polyps from endoscopy images have been described. However, the benefits of traditional CAD technologies in colonoscopy appear to be contradictory, therefore they should be improved to be ultimately considered useful. Recent advances in artificial intelligence (AI), deep learning (DL), and computer vision have shown potential to assist polyp detection during colonoscopy.
Average experienced endoscopists (each having performed <2000 screening colonoscopies) will perform the endoscopic procedure.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI | – | Device: AIArtificial intelligence colonoscopy |
| Control | – | –White light colonoscopy |
Who may qualify
- Ages
- 40 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Based on the observed prevalence of adenomas (35%) among patients undergoing colonoscopies at our center within the last 12 months, a sample size of 322 subjects per arm could allow for a 90% power to show the non-inferiority (primary end-point) of the AI-aided arm by excluding that the one-side 95% CI will exclude a difference of 10% in favour of the standard group. Such sample size will also have a 80% power to detect as statistical significant (α=0.05; two-sided test) a 10% absolute increase in the detection rate of adenomas in the AI-aided arm (secondary end-point). Performing a sub-stratification according to operator experience, we will planning to enroll the 322 subjects per arm performed by experts and 322 subjects per arm performed by average operator.
Key inclusion criteria (1 listed)
- All 40-80 years-old subjects undergoing a colonoscopy
Key exclusion criteria (5 listed)
- subjects with personal history of CRC, or IBD.
- patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale > 2 in any colonic segment).
- patients with previous colonic resection.
- patients on antithrombotic therapy, precluding polyp resection.
- patients who were not able or refused to give informed written consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AIArtificial intelligence colonoscopy | Device | AI |
Primary outcome
Non-inferiority of AI-aided colonoscopy in terms of ADR
Time frame: 5 Months
The proportion of participants with at least one adenoma (per-patient analysis).
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Feb 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alessandro Repici, MD principal investigator
Humanitas Research Hospital IRCCS, Rozzano-Milan
5 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colon Cancer, run by Istituto Clinico Humanitas at 5 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 5 Feb 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in Italy, Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Non-inferiority of AI-aided colonoscopy in terms of ADR (time frame: 5 Months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Feb 2021.