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Completed

NCT Number: NCT03822390

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

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.

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Key information

Conditions

Artificial Intelligence Colorectal Polyp

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Academic Medical Centre

Amsterdam, North Holland, 1105AZ, Netherlands

Who can participate

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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).

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.

    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

Secondary outcomes

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

    Time frame: 2 year

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

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

    Time frame: 2 year

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

  3. 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 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

  4. 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 number of diminutive polyps per colonoscopy that is resected and discarded without histopathological analysis with optical diagnosis strategy (the CAD-CNN system or endoscopist)

  5. 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 diminutive polyps are characterized based on optical diagnosis, removed and discarded without histopathological evaluation (i.e. proportion of polyps assessed with high confidence)

  6. 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 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

  7. The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.

    Time frame: 2 year

    The percentage of colonoscopies in which diminutive hyperplastic polyps in the rectosigmoid are left in situ.

  8. The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists

    Time frame: 2 year

    The diagnostic sensitivity for optical diagnosis of the CAD-CNN system and the endoscopists

  9. The diagnostic sensitiviy 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

  10. The accuracy rates on a per polyp basis

    Time frame: 2 year

    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.

  11. 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

    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

  12. The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists

    Time frame: 2 year

    The diagnostic specificity for optical diagnosis of the CAD-CNN system and the endoscopists

  13. The diagnostic PPV 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

  14. The diagnostic NPV 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

Sponsors and collaborators

Lead sponsor

Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

Other

Collaborators

  • Bergman Clinics
  • Frisius Medisch Centrum

Registry information

Official study title

Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition. A Multicentre, Prospective Observational Study

Acronym: POLAR

Important dates

Study start
2018
Primary completion
2021
Study completion
2021
First posted
Jan 30, 2019
Registry last updated
Dec 29, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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