ArticleDEN open2026
White Light, Magnifying Endoscopy, Endocytoscopy, and Artificial Intelligence in Diagnosis of Early Colorectal Cancer: A Comparative Study.
Article in DEN open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Early detection of colorectal cancer is critical for improving prognosis. However, assessing invasion depth-distinguishing between superficial cancer (T1a) and deep submucosal invasive cancer (T1b)-remains challenging. Recently, artificial intelligence (AI)-assisted computer-aided diagnosis (CADx) systems have been introduced to complement conventional endoscopy. This study aims to compare the diagnostic accuracy of endoscopists in predicting deep submucosal invasion in early colorectal cancer under four modalities: white-light imaging (WLI), magnifying endoscopy (including narrow-band imaging magnification and pit pattern), endocytoscopy (EC), and CADx support. Methods: We conducted a single-center retrospective study using stored endoscopic images between April 2021 and December 2022. Each lesion was evaluated using white light imaging, magnifying endoscopy, EC, and CADx analysis with the EndoBRAIN-Plus system. Trainee and expert endoscopists assessed the images sequentially, recording their estimations of invasion depth (T1a vs. T1b) and confidence levels. Sensitivity, specificity, and accuracy were calculated against the pathological reference. We compared performances stratified by confidence level and endoscopist experience. Results: During the study period, 66 lesions were eligible. Of them, 27% (18 lesions) were T1b cancers. Diagnostic accuracy improved progressively from white light imaging (82.7% [95% confidence interval {95%CI}: 81.2-86.9]) to EC (85.6% [95%CI: 82.7-88.2]). The highest specificity and accuracy were achieved when AI-assisted diagnosis was incorporated (accuracy: 88.9% [95%CI: 86.3-91.2], specificity: 93.1% [95%CI: 90.6-95.2]). The proportion of high-confidence readings rose from 40.2% to 75.5%. This was most pronounced in the trainee group. Conclusions: Integrating advanced endoscopic imaging with CADx significantly improved accuracy in assessing invasion depth. This approach may guide treatment decisions in early-stage colorectal cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.