Clinical trial NCT04349787: Improving Optical Diagnosis of Colorectal Polyps Using CADx and BASIC.
Official title: Improving Optical Diagnosis of Colorectal Polyps Using Computer-aided Diagnosis (CADx) and the BLI Adenoma Serrated International Classification (BASIC).
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colorectal Cancer
Colorectal Polyp
Keywords: Artificial intelligence
Computer-aided diagnosis
Optical diagnosis
Colorectal polyps
Blue light imaging
Lead sponsor: Maastricht University Medical Center
Sponsor class: OTHER
Collaborators: name: Catharina Ziekenhuis Eindhoven
name: Eindhoven University of Technology
First posted: 2020-04-16
Enrollment: 60
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who had a colonoscopy in the Catharina hospital Eindhoven between January 2017 and December 2018 in the context of the regular bowel cancer screening program, in the context of complaints or in the context of the follow-up of previously diagnosed bowel diseases.

Brief summary: Primary, this study aims to develop and validate a computer-aided diagnosis (CADx) system for the characterization of colorectal polyps.

Second, this study evaluates the effect of using a clinical classification model Blue Light Imaging Adenoma Serrated International (BASIC) on the diagnostic accuracy of the optical diagnosis of colorectal polyps compared to intuitive optical diagnosis for both expert endoscopists and novices.

Detailed description: Optical diagnosis of colorectal polyps, the in-vivo characterization of the histology by endoscopists, is of increasing interest for clinical endoscopy practice. Recent studies have shown that thresholds for optical diagnosis are met in highly selected groups of expert endoscopists, but the same is not true in community endoscopy practices. In order to improve optical diagnosis, imaging enhancement techniques and the use of artificial intelligence are proposed.

This observational study developes a computer-aided diagnosis (CADx) system to differentiate between benign and (pre-)malignant CRPs, using state-of-the-art machine learning methods and deep learning architectures. For the development, HDWL and BLI images are used. The CADx is trained using histology as gold standard. The CADx is externally validated using on a set of 60 colorectal polyps. This study will evaluate if the optical diagnosis of colorectal polyps can be improved with the aid of CADx.

In addition, both expert endoscopists and novices optically diagnose the colorectal polyps. In the first, pre-training phase, endoscopists optically diagnose colorectal polyps based on intuition. Afterwards, in the post-training phase, the same set of colorectal polyps is optically diagnosed based on a clinical classification system; BLI Adenoma Serrated International Classification (BASIC). This study will evaluate if the optical diagnosis of colorectal polyps can be improved with the aid of BASIC in both expert and non-expert hands.

Eligibility criteria: Inclusion Criteria:

* Patients with at least one colorectal polyp found and resected during colonoscopy in regular care;
* Availability of at least one high definition white light image and one Blue Light Imaging (BLI) image of the colorectal polyp;
* Overall high quality of the colorectal polyp image;
* Availability of the histological results of the colorectal polyp;
* Minimal age of 18 years old.

Exclusion Criteria:

* Objection to participate in medical scientific research, reported in the medical file;
* Endoscopic instruments visible at the colorectal polyp image;
* More than one polyp visible at the colorectal polyp image.

Primary outcomes:
measure: Diagnostic accuracy of CADx versus endoscopists
timeFrame: 6 months
Secondary outcomes:
measure: Diagnostic accuracy of BASIC versus intuition
timeFrame: 6 months
measure: Diagnostic metrics of CADx
timeFrame: 6 months
measure: AUC of CADx
timeFrame: 6 months
measure: Diagnostic metrics of endoscopists
timeFrame: 6 months
measure: Diagnostic metrics of endoscopists for high confidence diagnosis
timeFrame: 6 months
measure: Interobserver agreement
timeFrame: 6 months
measure: Computation time of CADx
timeFrame: 6 months
Other outcomes:
Locations:
city: Maastricht
country: Netherlands
facility: Maastricht University Medical Center
state: Limburg
Study officials:
affiliation: Maastricht Universitair Medisch Centrum
name: Ad Masclee, Prof. Dr.
role: PRINCIPAL_INVESTIGATOR
References:
citation: ASGE Technology Committee; Abu Dayyeh BK, Thosani N, Konda V, Wallace MB, Rex DK, Chauhan SS, Hwang JH, Komanduri S, Manfredi M, Maple JT, Murad FM, Siddiqui UD, Banerjee S. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2015 Mar;81(3):502.e1-502.e16. doi: 10.1016/j.gie.2014.12.022. Epub 2015 Jan 16.
citation: Subramaniam S, Hayee B, Aepli P, Schoon E, Stefanovic M, Kandiah K, Thayalasekaran S, Alkandari A, Bassett P, Coron E, Pech O, Hassan C, Neumann H, Bisschops R, Repici A, Bhandari P. Optical diagnosis of colorectal polyps with Blue Light Imaging using a new international classification. United European Gastroenterol J. 2019 Mar;7(2):316-325. doi: 10.1177/2050640618822402. Epub 2019 Jan 6.
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.
citation: van der Zander QEW, Schreuder RM, Fonolla R, Scheeve T, van der Sommen F, Winkens B, Aepli P, Hayee B, Pischel AB, Stefanovic M, Subramaniam S, Bhandari P, de With PHN, Masclee AAM, Schoon EJ. Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis. Endoscopy. 2021 Dec;53(12):1219-1226. doi: 10.1055/a-1343-1597. Epub 2021 Mar 10.