Evaluation of Quantitative Digital Pathology in the Assessment of Barrett Esophagus–Associated Dysplasia
Bibliographic record
Abstract
OBJECTIVES: Barrett esophagus (BE) is a precursor lesion that confers an increased risk of esophageal adenocarcinoma. Two issues confront the diagnosis of patients with BE: (1) sampling error at the time of endoscopy and (2) variability among pathologists in grading dysplasia. The purpose of our study was to evaluate quantitative digital pathology (QDP) as a marker of dysplasia and stratification from low-grade to high-grade dysplasia to intramucosal adenocarcinoma in BE. METHODS: Sixty-one esophageal biopsy specimens with BE were selected and divided into six groups according to the dysplasia grade. QDP image analysis was carried out by an in-house automated quantitative system on sections. The values of 110 nuclear features that analyze the morphology and chromatin texture were generated for each nucleus. RESULTS: A progressive correlation was found between nuclear morphometric features and chromatin features with BE dysplasia. The chromatin texture was the best discriminator of the class diagnosis. There was a significant difference between the chromatin features of isolated low-grade dysplasia vs low-grade dysplasia that was associated with higher grade lesions in other biopsy tissue fragments. CONCLUSIONS: QDP is a promising tool in the new era of digital pathology. Pending clinical validation studies, analysis of chromatin texture could contribute to the differential diagnosis of BE class and the detection of concomitant high-grade lesions if not sampled.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".