Intrinsic fluorescence spectroscopy for endoscopic detection and localization of the endobronchial cancerous lesions
Bibliographic record
Abstract
Fluorescence spectroscopy contains diagnostic information about the lung biochemistry and morphology, including tissue optical properties and fluorophores. However, the fluorophore information is generally masked by the optical properties of the tissue, which complicates the evaluation of their role in lung-cancer detection. In this work, we have developed a method for extracting the intrinsic fluorescence spectra from the endoscopic measurements of the combined fluorescence and reflectance spectra. Principle components and classification analysis was performed to evaluate the diagnostic potential of the extracted intrinsic fluorescence spectra from in vivo combined fluorescence and reflectance spectral measurements. We evaluated the diagnostic sensitivity and specificity of both the intrinsic fluorescence and the fluorescence spectra. The results showed that the intrinsic fluorescence spectra contain significant diagnostic information that had been masked by the lung optical properties. We have also found that the intrinsic fluorescence has improved the specificity for endobronchial-cancer detection, although with a slight decrease in the detection sensitivity, when compared to the fluorescence spectra. This may indicate that intrinsic fluorescence analysis could be used to improve the diagnostic specificity of fluorescence spectroscopy and imaging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".