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Record W2744074877 · doi:10.1055/s-0043-111790

A feasibility study of photoacoustic imaging of ex vivo endoscopic mucosal resection tissues from Barrett’s esophagus patients

2017· article· en· W2744074877 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEndoscopy International Open · 2017
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsSt. Michael's HospitalPrincess Margaret Cancer Centre
FundersDivision of Emerging FrontiersTerry Fox Research Institute
KeywordsEx vivoMedicineEsophagusDysplasiaPhotoacoustic imaging in biomedicineH&E stainBarrett's esophagusEndoscopic mucosal resectionIn vivoEndomicroscopyHistopathologyPathologyRadiologyEndoscopic ultrasoundStainingMuscular layerEndoscopyNuclear medicineConfocalSurgeryCancerInternal medicineAdenocarcinomaBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.311
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it