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

Colonoscopy Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03775811
• Other study ID #: HISINVIA
• Secondary ID: PI17/00894
• Status: Completed
• Start date: January 1, 2019
• Est. completion date: December 31, 2022

Study information

• Verified date: January 2023
• Source: Hospital Clinic of Barcelona
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Description:

Optical diagnosis aims to predict the histology of a polyp based on its endoscopic features. This practice could avoid histopathological analysis and reduce the derived costs. Under this premise, the American Society of Gastrointestinal Endoscopy (ASGE), in its Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) statement, established a diagnostic threshold for real-time endoscopic assessment of diminutive polyps. The rationale for its implementation is that the prevalence of advanced histology in polyps < 5mm is very low (0.5%).

Several studies have demonstrated that optical diagnosis of small polyps is safe and feasible in clinical practice and comparable to the current gold standard, histopathology. However, the accuracy of optical diagnosis has been shown to be insufficient in community-based practices or in non-expert hands and the diagnosis is even more difficult in diminutive polyps < 3 mm in which the discrepancy between the endoscopic and pathological diagnosis is about 15%.

Artificial Intelligence (AI) has emerged as a help tool for polyp characterization.

Aiming to improve optical diagnosis using AI methods, we propose a hybrid approach that combines DL with characteristics of polyps manually indicated by endoscopists (HybridAI).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 90
• Est. completion date: December 31, 2022
• Est. primary completion date: March 31, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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

Related Conditions & MeSH terms

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

Intervention

Other:
• AUTOMATED POLYP CLASSIFICATION
COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION

Locations

Country	Name	City	State
Spain	Hospital Clínic de Barcelona	Barcelona

Sponsors (2)

Lead Sponsor	Collaborator
Hospital Clinic of Barcelona	Instituto de Salud Carlos III

Country where clinical trial is conducted

Spain, 

References & Publications (3)

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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