A feasibility study of photoacoustic imaging of ex vivo endoscopic mucosal resection tissues from Barrett’s esophagus patients
Bibliographic record
Abstract
BACKGROUND AND STUDY AIMS: Accurate endoscopic detection of dysplasia in patients with Barrett's esophagus (BE) remains a major clinical challenge. The current standard is to take multiple biopsies under endoscopic image guidance, but this leaves the majority of the tissue unsampled, leading to significant risk of missing dysplasia. Furthermore, determining whether there is submucosal invasion is essential for proper staging. Hence, there is a clinical need for a rapid in vivo wide-field imaging method to identify dysplasia in BE, with the capability of imaging beyond the mucosal layer. We conducted an ex vivo feasibility study using photoacoustic imaging (PAI) in patients undergoing endoscopic mucosal resection (EMR) for known dysplasia. The objective was to characterize the esophageal microvascular pattern, with the long-term goal of performing in vivo endoscopic PAI for dysplasia detection and therapeutic guidance. MATERIALS AND METHODS: EMR tissues were mounted luminal side up. The tissues were scanned over a field of view of 14 mm (width) by 15 mm (depth) at 680, 750, and 850 nm (40 MHz acoustic central frequency). Ultrasound and photoacoustic images were simultaneously acquired. Tissues were then sliced and fixed in formalin for histopathology with hematoxylin and eosin staining. A total of 13 EMR specimens from eight patients were included in the analysis, which consisted of co-registration of the photoacoustic images with corresponding pathologist-classified histological images. We conducted mean difference test of the total hemoglobin distribution between tissue classes. RESULTS: Dysplastic and nondysplastic BE can be distinguished from squamous tissue in 84 % of region-of-interest comparisons (42/50). However, the ability of intrinsic PAI to distinguish dysplasia from NDBE, which is the clinically important challenge, was only about 33 % (10/30). CONCLUSION: We demonstrated the technical feasibility of this approach. Based on our ex vivo data, changes in total hemoglobin content from intrinsic PAI (i. e. without exogenous contrast) can differentiate BE from squamous esophageal mucosa. However, most likely intrinsic PAI is unable to differentiate dysplastic from nondysplastic BE with adequate sensitivity for clinical translation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".