Development of a New Simplified Tool to Predict LNPCPs Histology and Assess the Risk of Submucosal Invasive Cancer. The Colorectal Regular-Irregular Score (CRIS)
Colorectal cancer (CRC) is the third most common malignancy worldwide and the second leading cause of cancer related death. It can be prevented by endoscopic detection and complete resection of colorectal polyps. The JNET (Japanese NBI Expert Team) classification is clinically useful to predict the histology of large non-pedunculated colorectal polyps (LNPCPs) using narrow-band imaging at endoscopy. Japanese experts can reliably predict histology including the presence and depth of submucosal invasive cancer (SMI) using JNET with accuracy >87%. On the other hand, the International Evaluation of Endoscopy classification-JNET (IEE-JNET) group demonstrated that ESGE and JGES endoscopists had sufficient accuracy for JNET 1 (93.0%) but insufficient accuracy for JNET 2A/B and 3 (respectively 62.1%, 55.1% and 85.1%). Reliably distinguishing between JNET 2A, 2B and 3 has a profound clinical relevance, since JNET 2A lesions can safely be resected using pEMR whereas JNET 2B lesions should be resected en-bloc (EMR or ESD) due to the increased risk of cancer and JNET 3 lesions are preferably treated with surgery due to the high risk of deeply invasive carcinoma and the necessity of lymph node resection.
This study aims to validate a new simplified score, the Colorectal Regular-Irregular Score (CRIS) to fulfill the urgent need for a more effective and easier to use tool to predict LNPCPs histology. CRIS is a simplification of the JNET score which is mainly used by Japanese endoscopists or experts, recent evidence suggests its accuracy when used in everyday endoscopy in the Western world is insufficient. The investigators aim to compare JNET with CRIS for LNPCPs histology prediction amongst Western endoscopists using both original JNET interpretation and a clinically relevant approach.
The study consists of three work packages (WPs):
Work package one involves an expert online study where twelve expert endoscopists will evaluate 32 high-quality images of colorectal polyps using both JNET and CRIS classifications. Work package two involves an image/video-based online study where non-expert participants will be randomly assigned to rate images and videos using either JNET or CRIS, with performance re-evaluated after three months. Work package three involves a clinical study in a live endoscopy environment where non-expert endoscopists will participate in a randomized controlled trial assessing 10 colorectal polyps using either JNET or CRIS.
연구 개요
연구 유형
등록 (추정된)
단계
- 해당 없음
연락처 및 위치
연구 연락처
- 이름: David J Tate
- 전화번호: +3293321063
- 이메일: StudiesTissue.Resectie@uzgent.be
연구 장소
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Ghent, 벨기에, 9000
- UZ Gent
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참여기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
설명
Inclusion Criteria:
- Consenting Endoscopists of varying abilities and grades (endoscopist)
- Endoscopists who did not previously encounter the score (endoscopist)
- LNPCPs detected or referred for resection (patient)
Exclusion Criteria:
- Endoscopist does not consent to inclusion (endoscopist)
- Video of inadequate quality as per opinion of the principal investigator
- Endoscopist does not undergo learning intervention (endoscopist)
- Patient does not consent to data collection for the study (patient)
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
- 주 목적: 특수 증상
- 할당: 무작위
- 중재 모델: 크로스오버 할당
- 마스킹: 없음(오픈 라벨)
무기와 개입
참가자 그룹 / 팔 |
개입 / 치료 |
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활성 비교기: JNET Classification Training (Active Comparator)
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Participants will follow a 5-minute learning video (intervention) on JNET and later for CRIS.
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실험적: CRIS Classification Training (Experimental)
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Participants will follow a 5-minute learning video (intervention) on CRIS and later for JNET.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Diagnostic Accuracy of CRIS Classification for Submucosal Invasive Carcinoma (Sensitivity, Specificity, and Overall Accuracy)
기간: Immediately after 5-minute training and rating of 32 images (approximately 1 hour per participant)
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Sensitivity, specificity, and overall diagnostic accuracy of the CRIS classification for predicting submucosal invasive carcinoma compared with histopathological evaluation (reference standard).
Accuracy is calculated as the proportion of correct classifications (true positives + true negatives) divided by total assessments.
Results will be reported with 95% confidence intervals.
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Immediately after 5-minute training and rating of 32 images (approximately 1 hour per participant)
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Diagnostic Accuracy of CRIS Versus JNET Among Expert Endoscopists (Sensitivity, Specificity, Overall Accuracy)
기간: At completion of expert image rating (approximately 30 minutes per expert)
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Comparison of sensitivity, specificity, and overall diagnostic accuracy between CRIS and JNET classifications among 12 expert endoscopists rating 32 polyp images.
Accuracy calculated as proportion of correct histopathology predictions.
Results reported with 95% confidence intervals.
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At completion of expert image rating (approximately 30 minutes per expert)
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Diagnostic Accuracy of CRIS Versus JNET Across All Participant Categories (Sensitivity, Specificity, Overall Accuracy)
기간: Through study completion, an average of 3 years
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Comparison of sensitivity, specificity, and overall diagnostic accuracy between CRIS and JNET classifications across all participant categories (experts, consultants, trainees, medical students, endoscopy nurses).
Accuracy calculated as proportion of correct histopathology predictions using generalized linear mixed model analysis.
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Through study completion, an average of 3 years
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Change in Diagnostic Accuracy From Baseline to 3-Month Follow-up for CRIS Versus JNET (Sensitivity, Specificity, Overall Accuracy)
기간: 3 months after initial assessment
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Change in sensitivity, specificity, and overall diagnostic accuracy from immediate post-training assessment to 3-month delayed assessment for participants using CRIS versus JNET.
Reported as absolute change in accuracy with 95% confidence intervals.
A smaller decrease indicates better retention of classification skills.
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3 months after initial assessment
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Inter-observer Agreement for Polyp Classification Using CRIS Versus JNET (Fleiss' Kappa Coefficient)
기간: At completion of baseline image assessment (approximately 1 hour per participant)
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Inter-observer agreement among non-expert endoscopists for polyp classification using CRIS versus JNET, measured using Fleiss' kappa coefficient.
Values interpreted as: <0.20 poor, 0.21-0.40
fair, 0.41-0.60
moderate, 0.61-0.80
substantial, 0.81-1.00
almost perfect agreement.
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At completion of baseline image assessment (approximately 1 hour per participant)
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Correlation Between Proposed Endoscopic Treatment and CRIS/JNET Classification (Percentage Agreement)
기간: At completion of baseline image/video assessment (approximately 1 hour per participant)
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Percentage of cases where the proposed endoscopic treatment (piecemeal EMR, en-bloc EMR/ESD, or surgical referral) aligns with the guideline-recommended treatment based on CRIS or JNET classification.
Higher agreement indicates the classification effectively guides treatment selection.
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At completion of baseline image/video assessment (approximately 1 hour per participant)
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Change in CRIS/JNET Diagnostic Accuracy Following 5-Minute Structured Learning Intervention (Pre-Post Difference in Overall Accuracy)
기간: Immediately before and immediately after 5-minute learning video intervention (within a single 1-hour session)
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Change in overall diagnostic accuracy from pre-intervention baseline to immediately post-intervention for CRIS and JNET classifications.
Reported as absolute difference in accuracy percentage with 95% confidence intervals.
The CRIS/JNET classification uses a scale where accuracy ranges from 0% (no correct classifications) to 100% (all classifications correct), with higher scores indicating better diagnostic performance.
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Immediately before and immediately after 5-minute learning video intervention (within a single 1-hour session)
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공동 작업자 및 조사자
수사관
- 수석 연구원: David Tate, University Hospital, Ghent
연구 기록 날짜
연구 주요 날짜
연구 시작 (추정된)
기본 완료 (추정된)
연구 완료 (추정된)
연구 등록 날짜
최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
추가 정보
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