Clinical trial NCT03822390: Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition
Official title: Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition. A Multicentre, Prospective Observational Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Artificial Intelligence
Colorectal Polyp
Keywords: Artificial Intelligence
Computer aided diagnosis
Diminutive colorectal polyps
Optical diagnosis
Lead sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Sponsor class: OTHER
Collaborators: name: Bergman Clinics
name: Frisius Medisch Centrum
First posted: 2019-01-30
Enrollment: 292
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
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.

Brief 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. 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.


Eligibility criteria: Phase 1A -

- Patients with one polyp subtype (based on histology)

Phase 1B Patients older than 18 years that underwent colonoscopy in one of the participating centres.

Phase 2:- Validation CAD-CNN system

Inclusion Criteria:

All patients older than 18 years old undergoing screenings colonoscopy in one of the participating centres.

Exclusion Criteria:

* 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

Primary outcomes:
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.
timeFrame: 2 year
Secondary outcomes:
measure: The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.
timeFrame: 2 year
measure: The mean number of attempts of the CAD-CNN to make a diagnosis per polyp
timeFrame: 2 year
measure: 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
timeFrame: 2 year
measure: 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)
timeFrame: 2 year
measure: 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)
timeFrame: 2 year
measure: 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
timeFrame: 2 year
measure: The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.
timeFrame: 2 year
measure: The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists
timeFrame: 2 year
measure: The diagnostic sensitiviy for optical diagnosis of the CAD-CNN system and the endoscopists
timeFrame: 2 year
measure: The accuracy rates on a per polyp basis
timeFrame: 2 year
measure: 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
timeFrame: 2 year
measure: The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists
timeFrame: 2 year
measure: The diagnostic PPV for optical diagnosis of the CAD-CNN system and the endoscopists
timeFrame: 2 year
measure: The diagnostic NPV for optical diagnosis of the CAD-CNN system and the endoscopists
timeFrame: 2 year
Other outcomes:
Locations:
city: Amsterdam
country: Netherlands
facility: Academic Medical Centre
state: Noord-Holland
Study officials:
affiliation: Amsterdam UMC, location VUmc
name: Evelien NA Dekker, Msc
role: PRINCIPAL_INVESTIGATOR
References: