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Evalution of Optical Enhancement With Magnification for Polyop Histology

Evaluation of a New Image-enhanced Endoscopic Technology With Magnification Using I-scan Optical Enhancement for Prediction Colorectal Polyp Histology

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT02929641
Enrollment
100
Registered
2016-10-11
Start date
2016-08-31
Completion date
2018-02-28
Last updated
2017-12-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colorectal Polyps

Keywords

Optical Enhancement technology, coloractal polyp prediction

Brief summary

When a polyp is found, it woulf be recorded by a white-light and OE1 model with magnification for assessing and predicting its histology. After that, its really histology will be reported by an pathologist. When a polyp is found, OE mode 1 with magnification was first used with near focus the polyp and an endoscopist made a real-time prediction of polyp pathology. After that, high-definition mode and OE mode 1 without magnification were used to observe polyp sequencely. The video of all procedure was recorded.

Detailed description

After bowel cleansing with 2L polyethylene glycol, patients received colonoscopy under propofol intravenous anesthesia. When a polyp was found, we began to wash it and record the video. OE mode 1 with magnification was first used with near focus the polyp and an endoscopist made a real-time prediction of polyp pathology. After that, high-definition mode and OE mode 1 without magnification were used to observe polyp sequencely. Biopsy of the polyps were taken after observation, and the size of polyps was estimated by the biopsy forceps. The information of patient age, gender polyp morphology and location were recorded. After a video transcoding and clipping, the pathology type of each video clip was predicted by an endoscopist. The sensitivity, specificity, positive predicting value (PPV), negative predicting value (NPV) and accuracy were calculated. The pathology types of polyps were classified by non-neoplastic polyps and neoplastic polyps according to Vienna Classification. Statistics were performed by NCSS 11 and R software (Version 3.3.2).

Interventions

None listed

Sponsors

Shandong University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No

Inclusion criteria

* Patients will be included if they are found polyps during endoscopy procesure.

Exclusion criteria

* Inflammatory bowel disease * Biopsies were not available. * Unable to provide informed consent.

Design outcomes

Primary

MeasureTime frameDescription
To evaluated the accuracy of i-scan OE system with magnification endoscopy on real-time predicting the pathology types of colorectal polyps.18 monthsTo evaluated the accuracy of i-scan OE system with magnification endoscopy on real-time predicting the pathology types of colorectal polyps.

Secondary

MeasureTime frameDescription
To evaluate whether the diagnostic value of OE with magnification can meet the thresholds of resect-and-discard and diagnose-and-leave strategies outlined by Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) document.18 monthsTo evaluate whether the diagnostic value of OE with magnification can meet the thresholds of resect-and-discard and diagnose-and-leave strategies outlined by Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) document.
To assess the diagnostic value and inter-observer agreement of OE on post-hoc differentiating pathology of colorectal polyps.18 monthsTo assess the diagnostic value and inter-observer agreement of OE on post-hoc differentiating pathology of colorectal polyps.
To assess the value of magnification optical enhancement on classification of colorectal polyps using a deep learning model18 monthsTo assess the value of magnification optical enhancement on classification of colorectal polyps using a deep learning model

Countries

China

Contacts

Primary ContactYanqing Li, MD,PhD
liyanqing@sdu.edu.cn86-531-82169236

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026