Prediction of Diminutive/Small Polyp Histology: Didactic vs. Computer-based Training

Summary

Bowel cancer is one of the most common cancers and the best method of diagnosing it is through endoscopic examination of the bowel (colonoscopy). Pre-cursors of bowel cancer are called polyps which can be detected and removed at the time of the colonoscopy. This reduces the chance of developing bowel cancer. There are different types of polyp ranging from completely harmless to those that may develop into cancer over time.

Advances in technology mean more polyps are being detected and it is possible to predict the type of polyp. Therefore there is a new strategy in endoscopy whereby when a small polyp is detected, a prediction of polyp type is made, the polyp removed and then discarded rather than sending to the laboratory, thereby reducing costs to health services.

In the hands of experts, accuracies in predicting polyp type is similar to when the polyp is removed and sent to the lab for analysis. Whilst experts can do this, non-experts cannot reach these standards and there is a need for effective training.

The aim of the study is to compare the effectiveness of two training methods: Didactic face-to-face training and computer-based self-learning on the ability of trainees at predicting polyp type.

Conditions

• Colorectal Polyp

Interventions

OTHER

Endoscopic characterisation training

Included in the training material is an overview of "Resect and Discard", endoscopic platforms (NBI, BLI and i-Scan), NICE classification, SIMPLE classification, BASIC classification and example still images and videos of both classifications in use. Still images will be used to ensure participants have the best opportunity to observe and learn Kudo Pit Patterns and other polyp features without movement artefact before observing videos, which are more challenging to interpret. The training will last approximately 1 hour.

Sponsors & Collaborators

• Brigham and Women's Hospital

  collaborator OTHER

• Mount Sinai Hospital, New York

  collaborator OTHER

• Gunma University Graduate School of Medicine, Maebashi

  collaborator UNKNOWN

• Iwate Medical University

  collaborator OTHER

• Istituto Europeo di Oncologia

  collaborator OTHER

• Federico II University

  collaborator OTHER

• University of Erlangen-Nürnberg

  collaborator OTHER

• University of Birmingham

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2019-03-31

Primary Completion

2019-06-30

Completion

2019-12-31