Artificial IntelligencePOLARNCT03822390
Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition
An observational study of Artificial Intelligence and Colorectal Polyp, run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA) at 1 site in Netherlands.
Trial summary
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence, Colorectal Polyp
- What is being tested?
- CAD-CNN system
- Type of treatment
- Medical device
- How many people
- 292 participants
- Where
- 1 siteNetherlands
- Study dates
- 16 Oct 2018to 16 Oct 2021
- 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
- Adult, Older adult
Study population: Phase 1APatients that underwent colonoscopy between 2011-2018 in the Bergman Clinics Amsterdam, in the context of the Dutch bowel cancer screening or surveillance program or because of symptoms. Phase 1B Patients older than 18 years that underwent colonoscopy in one of the participating centres. Phase 2 All patients older than 18 years old undergoing screenings colonoscopy in one of the participating centres.
Who may be able to join
- All patients older than 18 years old undergoing screenings colonoscopy in one of the participating centres.
Who cannot join
- Diagnosis of inflammatory bowel disease, Lynch syndrome or (serrated) polyposis syndrome.
- Boston Bowel Preparation Scale (BBPS) <2 in one of the colon segments
- Patients who are unwilling or unable to give informed consent
Trial locations and contacts
1 site in 1 country.
Netherlands1 site
Academic Medical Centre
Amsterdam, North Holland, 1105AZ
About this study
Official title: Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition. A Multicentre, Prospective Observational Study
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking.
Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard.
Read more
Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2)
Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.
- Conditions
- Artificial Intelligence, Colorectal Polyp
- Being tested
- CAD-CNN system
- Keywords
- Artificial Intelligence, Computer aided diagnosis, Diminutive colorectal polyps, Optical diagnosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CAD-CNN systemDevice
The CAD-CNN system will be trained in predicting the histology of diminutive polyps. Before training, the dataset will be split up into a training set and a test set. To ensure a completely independent test and training set there will be no overlap between patients (i.e. if polyps from a patient A is present in the training set it cannot be in the test set as well).
| Study group | Role | What they receive |
|---|---|---|
| Patients | Patients older than 18 years undergoing colonoscopy in one the participating centres. Receives: Device: CAD-CNN system |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.
Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomized as neoplastic polyps, while HPs are considered non-neoplastic
Measured: 2 year
Secondary outcomes (14)
The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.
The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.
Measured: 2 year
The mean number of attempts of the CAD-CNN to make a diagnosis per polyp
The mean number of attempts of the CAD-CNN to make a diagnosis per polyp
Measured: 2 year
The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts
The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts
Measured: 2 year
The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)
The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)
Measured: 2 year
The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)
The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)
Measured: 2 year
The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy
The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy
Measured: 2 year
The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.
The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.
Measured: 2 year
The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists
The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists
Measured: 2 year
The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists
The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists
Measured: 2 year
The accuracy rates on a per polyp basis
Accuracy on a polyp basis is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy on a polyp basis, adenomas, SSLs and HPs are considered different subtypes.
Measured: 2 year
Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps
Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps
Measured: 2 year
The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists
The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists
Measured: 2 year
The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists
The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists
Measured: 2 year
The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists
The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists
Measured: 2 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jan 2019
- Study start16 Oct 2018
- Primary completion16 Oct 2021
- Study completion16 Oct 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)The 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
- 292 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Bergman Clinics, Frisius Medisch Centrum
- Responsible party
- Principal investigator, Prof. Evelien Dekker, MD, PhD, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
- Investigators
- Evelien NA Dekker, Msc, Principal investigator, Amsterdam UMC, location VUmc
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03822390
- Sponsor's protocol ID
- W18_422
- Acronym
- POLAR
- Status last verified
- Dec 2021
- First submitted
- 21 Jan 2019
- Last updated
- 29 Dec 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: There is not yet a plan to share data. However, patients will be asked informed consent with the possibility to share the data.
Publications
Houwen BBSL, Hazewinkel Y, Giotis I, Vleugels JLA, Mostafavi NS, van Putten P, Fockens P, Dekker E; POLAR Study Group. Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists. Endoscopy. 2023 Aug;55(8):756-765. doi: 10.1055/a-2009-3990. Epub 2023 Jan 9.
PubMed 36623839Houwen BBSL, Hartendorp F, Giotis I, Hazewinkel Y, Fockens P, Walstra TR, Dekker E; POLAR study group; *on behalf of the POLAR study group. Computer-aided classification of colorectal segments during colonoscopy: a deep learning approach based on images of a magnetic endoscopic positioning device. Scand J Gastroenterol. 2023 Jun;58(6):649-655. doi: 10.1080/00365521.2022.2151320. Epub 2022 Dec 2.
PubMed 36458659
Study at a glance
From the registry record, last updated by the sponsor on 29 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition. A Multicentre, Prospective Observational Study
- ClinicalTrials.gov ID
- NCT03822390
- Acronym / sponsor ID
- POLAR / W18_422
- Conditions
- Artificial Intelligence, Colorectal Polyp
- Keywords
- Artificial Intelligence, Computer aided diagnosis, Diminutive colorectal polyps, Optical diagnosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 292 participants (actual)
- Lead sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)University, hospital or other
- Collaborators
- Bergman Clinics, Frisius Medisch Centrum
- Study start
- 16 Oct 2018 (actual)
- Primary completion
- 16 Oct 2021 (actual)
- Study completion
- 16 Oct 2021 (actual)
- First posted
- 30 Jan 2019 (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.
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking.
Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard.
Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2)
Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients | – | Device: CAD-CNN systemPatients older than 18 years undergoing colonoscopy in one the participating centres. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Phase 1APatients that underwent colonoscopy between 2011-2018 in the Bergman Clinics Amsterdam, in the context of the Dutch bowel cancer screening or surveillance program or because of symptoms. Phase 1B Patients older than 18 years that underwent colonoscopy in one of the participating centres. Phase 2 All patients older than 18 years old undergoing screenings colonoscopy in one of the participating centres.
Key inclusion criteria (1 listed)
- All patients older than 18 years old undergoing screenings colonoscopy in one of the participating centres.
Key exclusion criteria (3 listed)
- Diagnosis of inflammatory bowel disease, Lynch syndrome or (serrated) polyposis syndrome.
- Boston Bowel Preparation Scale (BBPS) <2 in one of the colon segments
- Patients who are unwilling or unable to give informed 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 |
|---|---|---|
| CAD-CNN systemThe CAD-CNN system will be trained in predicting the histology of diminutive polyps. Before training, the dataset will be split up into a training set and a test set. To ensure a completely independent test and training set there will be no overlap between patients (i.e. if polyps from a patient A is present in the training set it cannot be in the test set as well). | Device | Patients |
Primary outcome
The accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.
Time frame: 2 year
Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and / or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomized as neoplastic polyps, while HPs are considered non-neoplastic
14 secondary outcomes
The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.
Time frame: 2 year
The mean number of attempts of the CAD-CNN to make a diagnosis per polyp
Time frame: 2 year
The ratio of unsuccessful diagnosis from all diagnosis of the CAD-CNN system. An unsuccessful diagnosis/failure of the CAD-CNN system is defined as more than 3 unsuccessful attempts
Time frame: 2 year
The number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)
Time frame: 2 year
The percentage of colonoscopies in which diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)
Time frame: 2 year
The percentage of colonoscopies in which the surveillance interval is based on the optical diagnosis of the CAD-CNN system and the patient can be directly informed of the surveillance interval after colonoscopy
Time frame: 2 year
The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.
Time frame: 2 year
The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists
Time frame: 2 year
The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists
Time frame: 2 year
The accuracy rates on a per polyp basis
Time frame: 2 year
Agreement between recommended surveillance intervals, based on optical diagnosis of diminutive polyps with high confidence, compared to surveillance recommendations based on histology of all polyps
Time frame: 2 year
The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists
Time frame: 2 year
The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists
Time frame: 2 year
The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists
Time frame: 2 year
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 29 Dec 2021.
- Derived from this study: PubMed 36623839Houwen BBSL, Hazewinkel Y, Giotis I, Vleugels JLA, Mostafavi NS, van Putten P, Fockens P, Dekker E; POLAR Study Group. Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists. Endoscopy. 2023 Aug;55(8):756-765. doi: 10.1055/a-2009-3990. Epub 2023 Jan 9.
- Derived from this study: PubMed 36458659Houwen BBSL, Hartendorp F, Giotis I, Hazewinkel Y, Fockens P, Walstra TR, Dekker E; POLAR study group; *on behalf of the POLAR study group. Computer-aided classification of colorectal segments during colonoscopy: a deep learning approach based on images of a magnetic endoscopic positioning device. Scand J Gastroenterol. 2023 Jun;58(6):649-655. doi: 10.1080/00365521.2022.2151320. Epub 2022 Dec 2.
- Individual participant dataSharing planned: Undecided. There is not yet a plan to share data. However, patients will be asked informed consent with the possibility to share the data.
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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.
Evelien NA Dekker, Msc principal investigator
Amsterdam UMC, location VUmc
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 Artificial Intelligence and Colorectal Polyp, run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA) at 1 site in Netherlands.
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 3 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 29 Dec 2021. 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 Netherlands. 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?
The accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions. (time frame: 2 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 NCT03822390, which also lists 2 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 29 Dec 2021.