Computer Aided Diagnosis (CADx) for Colorectal Polyps Resect-and-Discard Strategy

Study setting

This is an international, prospective, multi-centre, single-blind, non-inferiority, randomized controlled trial conducted in 6 university-affiliated endoscopy centres China (4 centres), Hong Kong and Singapore. This study will be conducted according to the CONSORT-AI and SPIRIT-AI guideline and complied with ICH-GCP and the declaration of Helsinki.

Artificial intelligence polyp characterization system

The investigators refer to the NICE (Narrow Band Imaging International Colorectal Endoscopic) classification as the standard to establish a deep neural network model for polyp type differentiation. To build the polyp characterization model, the investigators collected 3762 images of polyps under NBI for model training and testing, including 1483 cases of hyperplastic polyps, 1993 cases of adenomas and 286 cases of advanced tumors.

The difference of image features among the three types of NICE classification is obvious, and it is easy to distinguish them by computer under endoscope. Considering the processing capacity of the computer hardware equipped with the model, in order to achieve real-time analysis under limited computing resources, the investigators chose a lightweight network architecture called Mobile-Net to build the model. In a Mobile-Net framework, the first is a 3x3 standard convolution layer, followed by a heap of depth-wise separable convolution layers. Some of the depth-wise convolution layers will be down sampled through streets set as 2. The following average pooling layer changes the features to 1x1. According to the predicted category size, a full connection layer is then added, and finally a soft-max layer is added. If the depth-wise convolution layers and point-wise convolution layers are calculated separately, the entire network has only 28 layers ( Avg Pool and Softmax are not included). At present, the model has not been clinically validated. The investigators used a five-fold cross validation to evaluate the accuracy of the model. The results showed that the classification accuracy of the model in the current dataset exceeds 99%

Standardized training of optical diagnosis

Before the initiation of the study, participating endoscopists will be given a standardized online training workshop on the operation of the AI polyp characterization system, principles of optical diagnosis and an image-based quiz.

Colonoscopy procedures

Study colonoscopies will be performed by 12 non-expert endoscopists (colonoscopy experience <2000) and 12 expert endoscopists (colonoscopy experience ≥2000), with 2 non-expert and 2 expert endoscopists from each of the 6 centres. All colonoscopies will be performed by using high-definition endoscopy system and colonoscope (EVIS Lucera Elite, 290 series, Olympus, Co Ltd, Tokyo, Japan). All enrolled patients received low-fiber diets 3 days prior to colonoscopy and underwent bowel cleansing with polyethylene glycol solution in split dose based on institutional protocol. Colonoscopies will be performed under conscious sedation. Bowel preparation quality will be rated by the Boston Bowel Preparation Scale (BBPS) with adequate bowel preparation being defined as BBPS score ≥6 and any segmental BBPS score ≥2.

For patients randomized to the conventional colonoscopy (CC) with optical diagnosis group, the endoscopists will turn on narrow band imaging (NBI) mode once a polyp is detected. An optical diagnosis will be provided without magnification. Hyperplastic and sessile serrated polyps are categorized as non-neoplastic, adenoma and malignancy as neoplastic. The diagnosis will be recorded with the level of confidence of the assessment (high or low). For patients randomized to the AI-powered CADx colonoscopy (AI) group, endoscopists will turn on CADx upon detection of colorectal polyp. The polyp will then be marked by a blue tracking box and shown on the same high-definition monitor of the endoscopy system. The AI will provide a diagnosis (non-neoplastic or neoplastic) next to the tracking box. For the purpose of this study, all polyps irrespective of optical or AI diagnosis will be removed by endoscopic polypectomy/endoscopic mucosal resection (EMR)/endoscopic submucosal dissection (ESD) during withdrawal phrase and send for histopathology. Endoscopists are not allowed to leave any identified polyp in-situ at their discretion.

Location, size and morphology of all removed polyps will be documented. Proximal colon is defined as segment from cecum to transverse colon, while distal colon is defined as segment from splenic flexure to rectum. Polyps will be classified into pedunculated or non-pedunculated morphology and non-pedunculated lesions will be further characterized as sessile, flat or depressed lesions. Polyps removed will be placed in separate specimen bottles. Colonic polyp specimens will be evaluated by pathologists of individual study centre who are blinded to study group assignment. Colonic polyp specimens will be evaluated according to the 2019 WHO classification of tumours of the digestive system. Advanced adenomas are defined as adenomas with size ≥10mm, villous component, or high-grade dysplasia. Diagnostic yield of any existing colonic pathology will not be comprised if the subject is allocated to the AI group. Patients will be monitored for any immediate adverse event post colonoscopy and will be discharged after recovery from sedation.

Inclusion criteria

• undergoing elective colonoscopy with any indication (screening, surveillance or diagnostic) and complete colonoscopy (caecal intubation) with at least one colorectal polyp detected will be recruited

Exclusion criteria

• personal history of CRC or inflammatory bowel disease, prior colorectal surgery, receiving anticoagulant therapy
• lack of informed consent