Efficacy of Narrow Band Imaging System Combined With Magnifying Endoscopy for Differentiating Type IIa Early Gastric Cancer From Adenoma
Bibliographic record
Abstract
BACKGROUND: It is not always possible for endoscopists to differentiate early gastric cancer from adenoma in 0-IIa type neoplasia. The aim of this study was to assess the relationships between images obtained with a narrow band image system combined with magnifying endoscopy (MENBI) and histological findings, especially vascular patterns, to distinguish adenoma from type IIa early gastric cancer (EGC IIa). METHODS: We postoperatively confirmed and evaluated 46 elevated lesions, 32 adenomas and 14 EGC IIa in patients who had undergone endoscopic submucosal dissection. We randomly selected three sites from each neoplasm. The selected sites were classified as four irregular microvascular patterns (IMVPs). In addition, the selected sites were divided into two groups based on the presence of corkscrews. RESULTS: Regarding IMVP subcategories, (1) slight intrastructural irregular microvascular patterns (ISIMVPs) accounted for 84%, (2) severe ISIMVPs accounted for 6%, (3) fine networks (FNs) accounted for 10%, and (4) corkscrews accounted for 0 of cases in the adenomas. The corresponding proportions in the EGC IIa were (1) 24%, (2) 31%, (3) 45%, and (4) 0. Slight ISIMVPs, severe ISIMVPs, and FNs reliably distinguished the two diseases: P < 0.001 for slight ISMVPs; P < 0.001 for severe ISIMVPs; P < 0.001 for FNs. The presence of corkscrews was observed in 9.5% of EGC IIa and 0 of adenoma cases (P = 0.008). CONCLUSIONS: MENBI can be used to differentiate EGC IIa from gastric adenoma based on IMVPs classifications and the presence of corkscrews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".