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

Study Overview
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Brief Summary
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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.

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

Conditions
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Artificial Intelligence, Colorectal Polyp

Intervention / Treatment
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* Device: CAD-CNN system


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients<br>Patients older than 18 years undergoing colonoscopy in one the participating centres. | Device: CAD-CNN system<br>* 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).<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 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 | 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 | 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) | 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) | 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 | 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. | 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 | 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 | 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. | 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 | 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 | 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 | 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 | 2 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial Intelligence, Computer aided diagnosis, Diminutive colorectal polyps, Optical diagnosis