Clinical trial NCT03775811: In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy
Official title: In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colonoscopy
Histology
Computer-aided Diagnosis
Artificial Intelligence
Colorectal Polyp
Adenoma Colon Polyp
Hyperplastic Polyp
Lead sponsor: Hospital Clinic of Barcelona
Sponsor class: OTHER
Collaborators: name: Instituto de Salud Carlos III
First posted: 2018-12-14
Enrollment: 90
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

Detailed 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).

Eligibility criteria: 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

Primary outcomes:
measure: Accuracy of the computer-aided system for predicting polyps histology in real clinical practice
timeFrame: One year
Secondary outcomes:
Other outcomes:
Locations:
city: Barcelona
country: Spain
facility: Hospital Clínic de Barcelona
Study officials:
References:
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.
citation: 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.
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.