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

NCT ID: NCT06062095

Last Updated: 2023-10-11

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1764 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-09-29
• Study Completion Date: 2025-12-31

Brief Summary

Colonoscopic removal of adenomatous polyps reduce both the incidence and mortality of colorectal cancer (CRC). The common clinical management of colorectal polyp detected during colonoscopy is to remove them and send for histopathology to determine the subsequent surveillance interval. More than 80% of polyps detected during screening or surveillance colonoscopy are diminutive (≤5mm). As the chance of diminutive polyps to harbor cancer or advanced neoplasia is low, leave-in-situ and resect-and-discard strategies using optical diagnosis are recommended for non-neoplastic polyps by the American Society for Gastrointestinal Endoscopy (ASGE) and the European Society for Gastrointestinal Endoscopy (ESGE) so as to reduce the financial burden of polypectomy and histopathology. The societies proposed leave-in-situ strategy if optical diagnosis can achieve a negative predictive value (NPV) of >90% for rectosigmoid polyp and resect-and-discard if an agreement of more than 90% concordance with histopathology-based post-polypectomy surveillance interval can be achieved. However, optical diagnosis is operator dependent and most endoscopists are reluctant to adopt this strategy in routine practice because of the need of strict training and auditing and fear of incorrect diagnosis.

In the past decade, with the exponential increase in computational power, reduced cost of data storage, improved algorithmic sophistication, and increased availability of electronic health data, artificial intelligence (AI) assisted technologies were widely adopted in various healthcare settings to improve clinical outcomes, especially the quality of colonoscopy in the area of gastroenterology. Real time use of computer-aided diagnosis (CADx) for adenoma using AI systems were developed and proven to be useful to help endoscopists to distinguish neoplastic polyps from non-adenomatous polyps. However, these studies only examined diminutive polyp but not polyp of larger size (>5mm). They were conducted with small sample size of less than few hundred subjects and the study settings were open-label and non-randomized.

The investigators aim to conduct a large scale randomized controlled trial to evaluate the performance of colorectal polyp characterization of all size polyps by real-time CADx using AI system against conventional colonoscopy with optical diagnosis.

Detailed Description

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.

Conditions

Colonic Polyp

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

AI arm

AI will be used to diagnose polyps

Group Type: EXPERIMENTAL

AI-powered computer-aided diagnosis (CADx)

Intervention Type: PROCEDURE

Real time AI will be used to diagnosis polyps found during colonoscopy.

Control arm

Conventional colonoscopy without AI will be perform to diagnose polyps

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

AI-powered computer-aided diagnosis (CADx)

Real time AI will be used to diagnosis polyps found during colonoscopy.

Intervention Type: PROCEDURE

Eligibility Criteria

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

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Chinese University of Hong Kong

OTHER

Sponsor Role: lead

Responsible Party

Thomas Yuen Tung Lam

Assistant Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Combined Endoscopy Unit, ALice Ho Miu Ling Nethersole Hospital

Hong Kong, , Hong Kong

Site Status: RECRUITING

Countries

Hong Kong

Central Contacts

Felix SIA

Role: CONTACT

felixsia@cuhk.edu.hk

26370428

Facility Contacts

Felix Sia

Role: primary

26370428

Other Identifiers

2022.525

Identifier Type: -

Identifier Source: org_study_id