Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-26 @ 3:16 PM
NCT ID:
NCT07401004
Brief Summary:
This study investigates whether a brief educational intervention using Blink features can improve medical students' and non-GI trainees' ability to detect colorectal cancer in static polyp images. Secondary aims include evaluating changes in specificity, confidence, and interobserver agreement, determining which Blink features support accurate detection, and examining the link between the number of features recognized and diagnostic performance. The study will recruit medical students and non-GI trainees without prior training in polyp morphology or endoscopic image interpretation, who will complete an online pre- and post-intervention image-based survey.
Detailed Description:
Background and Rationale:
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality in Western countries, though it is largely preventable by detecting and removing precursor lesions such as colorectal polyps. While most polyps are small and benign, 1-2% are large (≥20 mm) non-pedunculated colorectal polyps (LNPCPs), which carry a markedly higher risk of invasive cancer (reported rates 6-15%, depending on morphology, histology, and location).
Accurate optical diagnosis of cancer in LNPCPs is critical for guiding treatment strategy-piecemeal endoscopic resection, en bloc resection, or surgery. However, endoscopists often underperform in identifying cancer within these lesions. Studies have reported correct cancer identification rates as low as 20-40%, even among trained endoscopists, contributing to unnecessary surgeries for benign polyps and missed diagnoses in malignant cases.
One contributing factor is the complexity of existing classification systems, which are rarely applied consistently in clinical practice. Simplified tools may improve accuracy and applicability.
The Blink framework, inspired by Malcolm Gladwell's concept of rapid, intuitive decision-making and aligned with Kahneman's System 1 thinking, condenses cancer recognition into six easily observed features of LNPCPs:
Fold deformation Extra redness Chicken skin mucosa Depression Spontaneous bleeding Ulceration
These Blink features can be recognized without advanced imaging and provide a structured, intuitive framework for rapid cancer detection. Previous research has shown that teaching these features improves endoscopists' diagnostic sensitivity. Building on this, the present study evaluates whether a brief Blink-based intervention can improve cancer detection among medical students and non-GI trainees with no prior training in polyp morphology.
Primary Objective:
To assess whether a short educational intervention (2-minute training video on Blink features) improves the sensitivity of medical students and non-GI trainees in detecting cancer in colorectal polyps using static images.
Secondary Objectives
* To evaluate changes in specificity, self-reported confidence, and interobserver agreement before and after the intervention.
* To identify which Blink features are associated with accurate cancer detection.
* To assess the relationship between the number of Blink features identified and diagnostic accuracy.
Study Design:
Design: Prospective interventional study with pre- and post-intervention assessments.
Setting: Online survey distributed to medical students and non-GI trainees affiliated with the Vrije Universiteit Brussel.
Intervention: 2-minute video training on the six Blink features, followed by re-assessment of images.
Target Population:
Medical students and non-GI trainees affiliated with the Vrije Universiteit Brussel without prior endoscopy experience.
Sample size: 50-100 participants (yielding 1,000-2,000 individual image evaluations).
Study:
NCT07401004
Study Brief:
Protocol Section:
NCT07401004