Multimodal tissue imaging: using coregistered optical tomography data to estimate tissue autofluorescence intensity change due to scattering and absorption by neoplastic epithelial cells
Bibliographic record
Abstract
Autofluorescence (AF) imaging provides valuable information about the structural and chemical states of tissue that can be used for early cancer detection. Optical scattering and absorption of excitation and emission light by the epithelium can significantly affect observed tissue AF intensity. Determining the effect of epithelial attenuation on the AF intensity could lead to a more accurate interpretation of AF intensity. We propose to use optical coherence tomography coregistered with AF imaging to characterize the AF attenuation due to the epithelium. We present imaging results from three vital tissue models, each consisting of a three-dimensional tissue culture grown from one of three epithelial cell lines (HCT116, OVCAR8, and MCF7) and immobilized on a fluorescence substrate. The AF loss profiles in the tissue layer show two different regimes, each approximately linearly decreasing with thickness. For thin cell cultures (<300 μm), the AF signal changes as AF(t)/AF(0)=1-1.3t (t is the thickness in millimeter). For thick cell cultures (>400 μm), the AF loss profiles have different intercepts but similar slopes. The data presented here can be used to estimate AF loss due to a change in the epithelial layer thickness and potentially to reduce AF bronchoscopy false positives due to inflammation and non-neoplastic epithelial thickening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".