Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition

NCT03822390 | Completed

• 1 other identifier: W18_422
• Study Type: observational
• Target: 292
• Locations: 1 country
• Sites: 1

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.

Conditions

Artificial Intelligence; Colorectal Polyp

Keywords

Artificial Intelligence; Computer aided diagnosis; Diminutive colorectal polyps; Optical diagnosis

Outcome Measures

Primary Outcomes (1)

• 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

  2 year

Secondary Outcomes (14)

• The mean duration in seconds of the CAD-CNN system to make a per polyp diagnosis.

  2 year

• The mean number of attempts of the CAD-CNN to make a diagnosis per polyp

  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

  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)

  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)

  2 year

Study Arms (1)

Patients

Patients older than 18 years undergoing colonoscopy in one the participating centres.

Device: CAD-CNN system

Interventions

CAD-CNN system DEVICE

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).

Patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA): lead
• Bergman Clinics: collaborator
• Frisius Medisch Centrum: collaborator

Study Sites (1)

Academic Medical Centre

Amsterdam, North Holland, 1105AZ, Netherlands

Location

Related Publications (2)

• 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.

  PMID: 36623839 DERIVED

• Houwen 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.

  PMID: 36458659 DERIVED

Study Officials

• Evelien NA Dekker, Msc

  Amsterdam UMC, location VUmc

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof. E. Dekker, MD, PhD

Study Record Dates

• First Submitted: January 21, 2019
• First Posted: January 30, 2019
• Study Start: October 16, 2018
• Primary Completion: October 16, 2021
• Study Completion: October 16, 2021
• Last Updated: December 29, 2021

Record last verified: 2021-12

Locations

NL (1)