In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy

NCT03775811 | Completed

• 2 other identifiers: HISINVIAPI17/00894
• Study Type: observational
• Target: 90
• Locations: 1 country
• Sites: 1

Brief Summary

Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.

Conditions

Colonoscopy; Histology; Computer-aided Diagnosis; Artificial Intelligence; Colorectal Polyp; Adenoma Colon Polyp; Hyperplastic Polyp

Outcome Measures

Primary Outcomes (1)

• Accuracy of the computer-aided system for predicting polyps histology in real clinical practice

  The results of the computer-aided system prediction will be compared with the final pathology report, which is the gold standard

  One year

Interventions

AUTOMATED POLYP CLASSIFICATION OTHER

COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients with polyps of any size/morphology, detected in a routine or screening colonoscopy, that are resected endoscopically and recovered for histological analysis will be included. The images obtained will be used to expand the database.

You may qualify if:

• Age \> 18 years
• Approval of participation in the study. Signature of informed consent
• Patients with at least one polyp of any size/morphology diagnosed in a routine or screening colonoscopy
• Endoscopies performed with high definition endoscopes

You may not qualify if:

• Age \<18 years
• Refusal to participate in the study
• Polyps partially resected in a previous endoscopy
• Patients with inflammatory disease
• Impossibility to wash remains of stool or mucus on the surface of the polyp

Sponsors & Collaborators

• Hospital Clinic of Barcelona: lead
• Instituto de Salud Carlos III: collaborator

Study Sites (1)

Hospital Clínic de Barcelona

Barcelona, 08036, Spain

Location

Related Publications (3)

• Sanchez-Montes C, Sanchez FJ, Bernal J, Cordova H, Lopez-Ceron M, Cuatrecasas M, Rodriguez de Miguel C, Garcia-Rodriguez A, Garces-Duran R, Pellise M, Llach J, Fernandez-Esparrach G. Computer-aided prediction of polyp histology on white light colonoscopy using surface pattern analysis. Endoscopy. 2019 Mar;51(3):261-265. doi: 10.1055/a-0732-5250. Epub 2018 Oct 25.

  PMID: 30360010 BACKGROUND

• Bernal J, Histace A, Masana M, Angermann Q, Sanchez-Montes C, Rodriguez de Miguel C, Hammami M, Garcia-Rodriguez A, Cordova H, Romain O, Fernandez-Esparrach G, Dray X, Sanchez FJ. GTCreator: a flexible annotation tool for image-based datasets. Int J Comput Assist Radiol Surg. 2019 Feb;14(2):191-201. doi: 10.1007/s11548-018-1864-x. Epub 2018 Sep 25.

  PMID: 30255462 BACKGROUND

• Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.

  PMID: 29066576 BACKGROUND

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: December 11, 2018
• First Posted: December 14, 2018
• Study Start: January 1, 2019
• Primary Completion: March 31, 2019
• Study Completion: December 31, 2022
• Last Updated: January 18, 2023

Record last verified: 2023-01

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)