ColonoscopyNCT03775811
In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy
An observational study of Colonoscopy and Histology, run by Hospital Clinic of Barcelona at 1 site in Spain.
Trial summary
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colonoscopy, Histology, Computer-aided Diagnosis
- What is being tested?
- AUTOMATED POLYP CLASSIFICATION
- How many people
- 90 participants
- Where
- 1 siteSpain
- Study dates
- 1 Jan 2019to 31 Dec 2022
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All patients with polyps of any size/morphology, detected in a routine or screening colonoscopy, that are resected endoscopically and recovered for histological analysis will be included. The images obtained will be used to expand the database.
Who may be able to join
- Age > 18 years
- Approval of participation in the study. Signature of informed consent
- Patients with at least one polyp of any size/morphology diagnosed in a routine or screening colonoscopy
- Endoscopies performed with high definition endoscopes
Who cannot join
- Age <18 years
- Refusal to participate in the study
- Polyps partially resected in a previous endoscopy
- Patients with inflammatory disease
- Impossibility to wash remains of stool or mucus on the surface of the polyp
Trial locations and contacts
1 site in 1 country.
About this study
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images.
Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Read the detailed description
Optical diagnosis aims to predict the histology of a polyp based on its endoscopic features. This practice could avoid histopathological analysis and reduce the derived costs. Under this premise, the American Society of Gastrointestinal Endoscopy (ASGE), in its Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) statement, established a diagnostic threshold for real-time endoscopic assessment of diminutive polyps. The rationale for its implementation is that the prevalence of advanced histology in polyps < 5mm is very low (0.5%).
Several studies have demonstrated that optical diagnosis of small polyps is safe and feasible in clinical practice and comparable to the current gold standard, histopathology. However, the accuracy of optical diagnosis has been shown to be insufficient in community-based practices or in non-expert hands and the diagnosis is even more difficult in diminutive polyps < 3 mm in which the discrepancy between the endoscopic and pathological diagnosis is about 15%.
Artificial Intelligence (AI) has emerged as a help tool for polyp characterization.
Aiming to improve optical diagnosis using AI methods, we propose a hybrid approach that combines DL with characteristics of polyps manually indicated by endoscopists (HybridAI).
- Conditions
- Colonoscopy, Histology, Computer-aided Diagnosis, Artificial Intelligence, Colorectal Polyp, Adenoma Colon Polyp
- Being tested
- AUTOMATED POLYP CLASSIFICATION
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AUTOMATED POLYP CLASSIFICATIONOther
COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of the computer-aided system for predicting polyps histology in real clinical practice
The results of the computer-aided system prediction will be compared with the final pathology report, which is the gold standard
Measured: One year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Dec 2018
- Study start1 Jan 2019
- Primary completion31 Mar 2019
- Study completion31 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for ColonoscopyStudies of the same condition, open and under way.
- More trials run by Hospital Clinic of BarcelonaThe sponsor's other registered studies.
- 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
- 90 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospital Clinic of Barcelona
- Sponsor type
- University, hospital or other
- Collaborators
- Instituto de Salud Carlos III
- Responsible party
- Principal investigator, Ana García-Rodríguez, Hospital Clinic of Barcelona
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03775811
- Sponsor's protocol ID
- HISINVIA
- Other IDs
- PI17/00894
- Status last verified
- Jan 2023
- First submitted
- 11 Dec 2018
- Last updated
- 18 Jan 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Sanchez-Montes C, Sanchez FJ, Bernal J, Cordova H, Lopez-Ceron M, Cuatrecasas M, Rodriguez de Miguel C, Garcia-Rodriguez A, Garces-Duran R, Pellise M, Llach J, Fernandez-Esparrach G. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019 Mar;51(3):261-265. doi: 10.1055/a-0732-5250. Epub 2018 Oct 25.
PubMed 30360010Bernal J, Histace A, Masana M, Angermann Q, Sanchez-Montes C, Rodriguez de Miguel C, Hammami M, Garcia-Rodriguez A, Cordova H, Romain O, Fernandez-Esparrach G, Dray X, Sanchez FJ. GTCreator: a flexible annotation tool for image-based datasets. Int J Comput Assist Radiol Surg. 2019 Feb;14(2):191-201. doi: 10.1007/s11548-018-1864-x. Epub 2018 Sep 25.
PubMed 30255462Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
PubMed 29066576
Study at a glance
From the registry record, last updated by the sponsor on 18 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy
- ClinicalTrials.gov ID
- NCT03775811
- Acronym / sponsor ID
- HISINVIA
- Conditions
- Colonoscopy, Histology, Computer-aided Diagnosis, Artificial Intelligence, Colorectal Polyp, Adenoma Colon Polyp
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 90 participants (actual)
- Lead sponsor
- Hospital Clinic of BarcelonaUniversity, hospital or other
- Collaborators
- Instituto de Salud Carlos III
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 31 Mar 2019 (actual)
- Study completion
- 31 Dec 2022 (actual)
- First posted
- 14 Dec 2018 (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.
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images.
Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Detailed description
Optical diagnosis aims to predict the histology of a polyp based on its endoscopic features. This practice could avoid histopathological analysis and reduce the derived costs. Under this premise, the American Society of Gastrointestinal Endoscopy (ASGE), in its Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) statement, established a diagnostic threshold for real-time endoscopic assessment of diminutive polyps. The rationale for its implementation is that the prevalence of advanced histology in polyps < 5mm is very low (0.5%).
Several studies have demonstrated that optical diagnosis of small polyps is safe and feasible in clinical practice and comparable to the current gold standard, histopathology. However, the accuracy of optical diagnosis has been shown to be insufficient in community-based practices or in non-expert hands and the diagnosis is even more difficult in diminutive polyps < 3 mm in which the discrepancy between the endoscopic and pathological diagnosis is about 15%.
Artificial Intelligence (AI) has emerged as a help tool for polyp characterization.
Aiming to improve optical diagnosis using AI methods, we propose a hybrid approach that combines DL with characteristics of polyps manually indicated by endoscopists (HybridAI).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All patients with polyps of any size/morphology, detected in a routine or screening colonoscopy, that are resected endoscopically and recovered for histological analysis will be included. The images obtained will be used to expand the database.
Key inclusion criteria (4 listed)
- Age > 18 years
- Approval of participation in the study. Signature of informed consent
- Patients with at least one polyp of any size/morphology diagnosed in a routine or screening colonoscopy
- Endoscopies performed with high definition endoscopes
Key exclusion criteria (5 listed)
- Age <18 years
- Refusal to participate in the study
- Polyps partially resected in a previous endoscopy
- Patients with inflammatory disease
- Impossibility to wash remains of stool or mucus on the surface of the polyp
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 |
|---|---|---|
| AUTOMATED POLYP CLASSIFICATIONCOLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION | Other | – |
Primary outcome
Accuracy of the computer-aided system for predicting polyps histology in real clinical practice
Time frame: One year
The results of the computer-aided system prediction will be compared with the final pathology report, which is the gold standard
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 18 Jan 2023.
- Background: PubMed 30360010Sanchez-Montes C, Sanchez FJ, Bernal J, Cordova H, Lopez-Ceron M, Cuatrecasas M, Rodriguez de Miguel C, Garcia-Rodriguez A, Garces-Duran R, Pellise M, Llach J, Fernandez-Esparrach G. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019 Mar;51(3):261-265. doi: 10.1055/a-0732-5250. Epub 2018 Oct 25.
- Background: PubMed 30255462Bernal J, Histace A, Masana M, Angermann Q, Sanchez-Montes C, Rodriguez de Miguel C, Hammami M, Garcia-Rodriguez A, Cordova H, Romain O, Fernandez-Esparrach G, Dray X, Sanchez FJ. GTCreator: a flexible annotation tool for image-based datasets. Int J Comput Assist Radiol Surg. 2019 Feb;14(2):191-201. doi: 10.1007/s11548-018-1864-x. Epub 2018 Sep 25.
- Background: PubMed 29066576Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
- Individual participant dataSharing planned: No
Related trials for Colonoscopy
Not yet recruiting
Effect of Box Breathing Exercise on Anxiety, Pain, and Pulmonary Function Tests in Patients Undergoing Colonoscopy
Colonoscopy
RecruitingObservational study
Characterization of Colorectal Dysplasic Lesions and Stratification of Their Evolving Risk in Inflammatory Bowel Disease Patients: a Prospective, Observational and Multicenter Study
Crohn Disease (CD), Colitis Ulcerative
Not yet recruiting
HFNC vs Conventional Oxygen During Gastrocolonoscopy in Elderly Patients
Hypoxemia, Eldery People
Recruiting
Five or Ten Year Colonoscopy for 1-2 Non-Advanced Adenomatous Polyps
Adenocarcinoma of the Colon, Adenocarcinoma of the Rectum
Recruiting
Visual Aids for Colonoscopy Preparation: A Prospective Study on Patient Understanding and Bowel Cleanliness in Colonoscopy Patients
The Effect of Audio-visual Aids on Bowel Cleanliness, Audiovisual Aids
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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colonoscopy and Histology, run by Hospital Clinic of Barcelona at 1 site in Spain.
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 4 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 18 Jan 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. 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?
Accuracy of the computer-aided system for predicting polyps histology in real clinical practice (time frame: One year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03775811, which also lists 3 linked publications.
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 18 Jan 2023.