Introduction
Optical identification of submucosal invasive cancer in large non-pedunculated colorectal polyps determines resection strategy, but established classifications depend on magnification and specialist vocabulary and are applied inconsistently outside expert practice. We evaluated a prespecified three-step algorithm combining a white-light gross impression (Blink), a demarcated area and the Colorectal Regular-Irregular Score (CRIS) vascular pattern, across assessors of widely differing experience.
Aims & Methods
We conducted a prospective diagnostic-accuracy study at four European tertiary centres between August 2023 and November 2025. Consecutive adults referred for endoscopic resection of a non-pedunculated colorectal lesion ≥20 mm were eligible. Assessors included experts (>200 lifetime advanced resections), fellows, trainees, nurse endoscopists, and medical students. The procedural endoscopist controlled the scope, while other assessors independently evaluated the same live display and completed a standardised form before resection or discussion.
Assessors recorded, in fixed order: Blink, considered positive when at least two of six gross white-light features were present; a demarcated area, defined as an abrupt change in surface or vascular pattern; and CRIS, which classified vessels as regular, regularly irregular, or irregular. The algorithm was positive if Blink was positive, regardless of subsequent findings, or if Blink was negative but a demarcated area contained a non-regular vascular pattern. Blinded expert histopathology was the reference standard; invasion ≥1000 µm, or beyond was classified as deeply invasive cancer (pT1b or greater). The primary analysis estimated assessment-level performance, with 95% confidence intervals (CI) calculated using a two-way cluster bootstrap resampling lesions and assessors independently. A lesion-level analysis used the majority assessment, with ties classified as positive.
Results
Overall, 256 large non-pedunculated colorectal polyps from 255 patients were analysed, comprising 537 live assessments by 32 assessors. 49 lesions contained submucosal invasive cancer (19.1%), including 36 with pT1b-or-greater invasion.
At assessment level, sensitivity for submucosal invasive cancer was 91.8% (95% CI 79.8–99.3) and specificity 62.1% (95% CI 51.4–71.4). For pT1b-or-greater cancer, sensitivity was 95.3% (95% CI 85.1–100), with a negative predictive value of 98.5% (95% CI 95.3–100). In the lesion-majority analysis, sensitivity was 89.8% (95% CI 80.5–97.8) and specificity 63.8% (95% CI 57.2–69.5).
Blink identified 91 of 110 cancer-positive assessments. Among the 19 cancers missed by Blink, assessment of the demarcated area with a non-regular vascular pattern correctly identified a further 10. Inter-observer agreement for the complete algorithm was substantial (Krippendorff’s α 0.65). Experts had higher specificity than non-experts (73.3% vs 56.8%; difference 16.5 percentage points, 95% CI 0.4–33.0), while sensitivity was similar between groups (difference 3.6 percentage points, 95% CI −11.0 to 21.3).
Conclusion
A three-step algorithm requiring neither magnification nor specialist terminology identified submucosal invasive cancer, including deep invasion, with high sensitivity and negative predictive value during live assessment by observers ranging from medical students to experts. Sensitivity was preserved across experience levels, whereas specificity was higher among experts. The algorithm should be considered a triage tool to identify lesions requiring expert assessment and resection planning, rather than a standalone treatment rule.
Disclosure
D.J.Tate: Pentax Medical, Fujifilm, and Olympus research support and consulting.
The other authors: no conflicts of interest.