Can optical evaluation distinguish between T1a and T1b esophageal adenocarcinoma: an international expert interobserver agreement study
Bibliographic record
Abstract
Background Piecemeal endoscopic mucosal resection (EMR) is an acceptable technique for T1a esophageal adenocarcinoma, but en bloc R0 excision is advocated for T1b disease as it may offer a potential cure and mitigate recurrence. Thus, distinguishing between T1a and T1b disease is imperative under current treatment paradigms. We investigated whether expert Barrett’s endoscopists could make this distinction based on optical evaluation. Methods Endoscopic images of histologically confirmed high grade dysplasia (HGD), T1a, and T1b disease (20 sets for each) were compiled from consecutive patients at a single institution. Each set contained four images including an overview, a close-up in high definition white light, a near-focus magnification image, and a narrow-band image. Experts predicted the histology for each set. Results 19 experts from 8 countries (Australia, USA, Italy, Netherlands, Germany, Canada, Belgium, and Portugal) participated. The majority had been practicing for > 20 years, with a median (interquartile range) annual case volume of 50 (18–75) for Barrett’s EMR and 25 (10–45) for Barrett’s endoscopic submucosal dissection. Esophageal adenocarcinoma (T1a/b) could be distinguished from HGD with a pooled sensitivity of 89.1 % (95 %CI 84.7–93.4). T1b adenocarcinoma could be predicted with a pooled sensitivity of 43.8 % (95 %CI 29.9–57.7). Fleiss’ kappa was 0.421 (95 %CI 0.399–0.442; P < 0.001), indicating fair-to-moderate agreement. Conclusions Expert Barrett’s endoscopists could reliably differentiate T1a/T1b esophageal adenocarcinoma from HGD. Despite fair-to-moderate agreement for T staging, T1b disease could not be reliably distinguished from T1a disease. This may impact clinical decision making and selection of endoscopic techniques.
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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 | 0.070 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".