Characterization of Barrett esophagus and esophageal adenocarcinoma by Fourier-transform infrared microscopy
Bibliographic record
Abstract
The objective of this exploratory study was to evaluate the feasibility of using Fourier-Transform Infrared (FTIR) spectromicroscopy to characterize formalin-fixed, paraffin-embedded human esophageal tissues. Matched histologically normal esophageal squamous epithelium (NS), premalignant Barrett esophagus (BE), and primary esophageal adenocarcinoma (EADC) tissues, each defined according to strict clinicopathologic criteria, were obtained from patients who underwent esophageal resection. Using confocal IR microscopy, measurements in the mid-IR spectral region were carried out in transflection configuration, scanning regions of interest in 15 microm steps. A multidimensional dataset reporting the spectroscopic properties at each sampled point were analyzed by performing a hierarchical cluster analysis on the second derivative of spectral traces. Normal esophageal epithelia were characterized by a few well defined regions, mostly of large size (tens of contiguous pixels), which correlated with tissue histology, specifically the basal cell layer. BE tissues had characteristic regions localized to gland crypts, ranging in size from one pixel to a few tens of pixels, which displayed IR spectra with defined absorption features characteristic of glycoproteins. The incorporation of synchrotron light to improve the resolution of individual cells in BE tissues has demonstrated that these glycoproteins are associated with goblet cells, the characteristic cell type defining BE. Whereas the highly fragmented regions identified in EADC likely reflect tumor heterogeneity, FTIR mapping would appear to be a potentially useful technique to identify premalignant BE tissues. The technical feasibility of using FTIR to characterize formalin-fixed, paraffin-embedded human esophageal tissues demonstrates the potential of this technique to study archival human BE tissue specimens via automated screening techniques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".