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| ID | Type | Description | Link |
|---|---|---|---|
| 25311_Blink | Other Identifier | UZ Brussel |
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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.
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
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).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Single Arm: Blink Features Training | Experimental | Medical students and non-GI trainees complete a baseline assessment of colorectal polyp images, receive a brief 2-minute educational video on Blink features, and then complete a post-intervention assessment. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Blink Features Training Video | Behavioral | A brief (2-minute) educational video introducing six Blink features (fold deformation, extra redness, chicken skin mucosa, depression, spontaneous bleeding, ulceration) to improve recognition of colorectal cancer in large non-pedunculated colorectal polyps. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in sensitivity for detection of colorectal cancer in polyp images | Sensitivity will be calculated as the proportion of correctly identified cancerous polyps out of all cancerous polyps presented. Comparison will be made between pre-intervention and post-intervention assessments. | Immediately before and after the educational intervention (within one online survey session, approximately 15-20 minutes). |
| Measure | Description | Time Frame |
|---|---|---|
| Change in specificity for cancer detection | Specificity: proportion of cancerous polyps correctly identified as cancerous. Compared pre- vs post-intervention using the same participant as their own control. | Immediately before and after the educational intervention (single online session, ~15-20 minutes). |
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Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| UZ Brussel | Jette | Brussels Capital | 1090 | Belgium |
De-identified individual participant data (IPD) underlying the reported results will be made available upon reasonable request from qualified researchers after publication of the study results. Data will be shared directly by the principal investigator following a data access agreement
De-identified IPD and supporting information will be available starting 6 months after publication of the primary results and will remain available for 5 years.
Data will be shared with qualified researchers upon reasonable request and after signing a data access agreement.
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| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| ID | Term |
|---|---|
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
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Single group of medical students/non-GI trainee's will complete a pre-intervention assessment of colorectal polyp images, receive a brief educational intervention (2-minute Blink features video), and then complete a post-intervention assessment using the same images again.
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|
| Change in self-reported diagnostic confidence |
Mean confidence score per case (e.g., 1-5 Likert scale) averaged per participant, compared pre- vs post-intervention. |
| Immediately before and after the educational intervention (same session). |
| Change in interobserver agreement (Fleiss' kappa) | Agreement among participants on cancer vs non-cancer classification, computed as Fleiss' kappa and compared pre- vs post-intervention | Immediately before and after the educational intervention (same session). |
| Correlation between presence of individual Blink features and correct cancer classification | For each endoscopic image, participants will indicate the presence or absence of predefined Blink features (fold deformation, extra redness, chicken skin mucosa, depression, spontaneous bleeding, ulceration). Each feature will be analyzed separately. The presence of each feature will be correlated with diagnostic accuracy, defined as the proportion (%) of correct classifications (cancer vs non-cancer) across participants, using histology as the reference standard. Measurement Tool: Online survey with image annotation for Blink features and classification task for cancer vs non-cancer. Unit of Measure: Correlation coefficient (r) between presence/absence of each Blink feature and diagnostic accuracy (%). | Baseline (pre-intervention) and immediately after the educational intervention (within the same online survey session, ~15-20 minutes). |
| Correlation between number of Blink features identified and diagnostic accuracy | For each image, participants will indicate the presence or absence of predefined Blink features (fold deformation, extra redness, chicken skin mucosa, depression, spontaneous bleeding, ulceration). The per-image/per-participant count of features identified will then be correlated with diagnostic accuracy (% of correct cancer vs non-cancer classifications). Measurement Tool: Online survey with image annotation for Blink features and classification task for cancer vs non-cancer (histology as reference standard). Unit of Measure: Correlation coefficient (r) between number of Blink features identified and diagnostic accuracy (%). | Immediately after the educational intervention (within the same online survey session, ~15-20 minutes). |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |