Linear‐array endoscopic ultrasound improves the accuracy of preoperative submucosal invasion prediction in suspected early gastric cancer compared with radial endoscopic ultrasound: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND AND AIM: There is a lack of literature comparing linear endoscopic ultrasound (EUS) and radial EUS for the prediction of the depth of invasion in early gastric cancer (EGC). The aim of this study is to evaluate the accuracy of linear EUS for the diagnosis of submucosal (SM) invasion and compare linear EUS with radial EUS in suspected EGC patients. METHODS: Seventy-two consecutive patients with suspected EGC who underwent a preoperative assessment using linear EUS or radial EUS were prospectively enrolled. The depth of invasion was categorized into mucosal to SM (< T1b) and SM or deeper (≥ T1b), and the EUS-determined diagnosis was compared with postoperative histopathological findings. RESULTS: Thirty-nine patients underwent radial EUS, and 33 patients underwent linear EUS examination. The baseline characteristics between the groups were well balanced. The diagnostic accuracy was much higher for patients who underwent linear EUS compared with radial EUS (90.9% vs 69.2%, P = 0.024). The sensitivity was 92.3% (95% confidence interval [CI] 66.7-98.6%) for linear EUS and 90.9% (95% CI 62.3-98.4%) for radial EUS. The specificity was 90.0% (95% CI 69.9-97.2%) in the linear EUS group, while the specificity was 60.7% (95% CI 42.4-76.4%) in the radial EUS group. Univariate analysis showed that EUS type (odds ratio 0.225, 95% CI 0.057-0.884, P = 0.033) was an associated risk factor of incorrect T1b staging in EGC patients. The area under the receiver operating curve was 0.912 and 0.758 for linear and radial EUS, respectively. CONCLUSION: Linear EUS was more accurate for determining SM invasion and therapeutic strategy in suspected EGC patients compared with radial EUS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".