Polyp Histology Prediction by Artificial Intelligence

Colorectal Polyp Clinical Trial

Official title:

Colorectal Polyp Histology Prediction by Artificial Ingelligence Method Based on NBI Colonoscopy Images.

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05545189
• Other study ID #: PetzACTH2
• Status: Not yet recruiting
• Start date: October 31, 2022
• Est. completion date: March 31, 2024

Study information

• Verified date: September 2022
• Source: Petz Aladar County Teaching Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging(NBI) colonoscopy images to predict the hyperplastic or neoplastic histology of polyps.

We plan to study colonoscopy polyp samples taken by polypectomy from 1200 patients.The documented NBI still images will be analyzed by the AIPHP method and by the NICE classification parallel.Our aim is to analyze the accuracy of AIPHP and NBI classification based histology predictions and also compare the results of the two methods.

Description:

Background:

Colonoscopy with polypectomy or early colorectal neoplastic lesions (polyp) is a proven and widely accepted method of reducing colorectal cancer mortality rates.

Predicting histology prior to endoscopic colorectal polyp removal is useful especially for diminutive (1-5mm) and small (6-10mm ) polyps.

Evaluation of colorectal polyps using the narrow-band imaging (NBI) technique and the NBI International Colorectal Endoscopic (NICE) classification are useful to predict the histology during endoscopy.However, NBI and magnification based polyp histology prediction needs training and endoscopic experience. Morever , the final and objective diagnosis still requires histology.

Therefore,we have been developing arteficial intelligence-based polyp histology prediction (AIPHP) software to automatically evaluate the magnified NBI colonoscopy images aiming the histology prediction of polyps.

Materials and methods:

We plan to examine 1200 colorectal polyps obtained from patients. Polyps will be removed by traditional polypectomy or with mucosectomy.Endoscopic procedures and histological examinations performed at the participation hospitals. Colonoscopy will be performed with Olympus EXERA III CFHQ190I (Olympus ,Tokyo,Japan) high reolution NBI colonoscopes providing 65x optical magnification. Colorectal polyps will be detected first by high definition colonoscopy then by NBI at the optical maximum magnification (65x). All studied polyps will be photo-documented. The stored NBI photos were anelyzed by the NICE classification and AIPHP parallel system.

Histological examination methods:

We use WHO classification of colorectal polyps. The two -class classification will considere hyperplastic or neoplastic ((SSLs,tubular or villous adenomas, and invasive adenocarcinomas).

AIPHP software systtem:

The AIPHP software is based on the categorization of the vascular pattern and color of the polyps.The main steps of AIPHP software development will be the following: 1) feature vector calculation 2) training of classifier module, and 3) AIPHP classifier testing. Five features will be used by our AIPHP software.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1200
• Est. completion date: March 31, 2024
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 85 Years

Eligibility

Inclusion Criteria:

• colorectal polyps removed by polypectomy

Exclusion Criteria:

• colorectal polyps with IBD

Related Conditions & MeSH terms

• Colorectal Polyp
• Polyps

Intervention

Diagnostic Test:
• colonoscopy,polypectomy
polyp removal during colonoscopy

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Petz Aladar County Teaching Hospital

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Polyp histology accuracy by AI method	polyp histology prediction by AI	two weeks