Combining field imaging endoscopy with point analysis spectroscopy for improving early lung cancer detection
Bibliographic record
Abstract
We propose to combine field imaging endoscopy with point spectral analysis for improving the overall diagnostic accuracy in clinical lung cancer detection. For this purpose, we developed an integrated endoscopy system that uses autofluorescence imaging and white light reflectance imaging to obtain high diagnostic sensitivity, while at the same time uses non-contact point reflectance/fluorescence spectroscopy to reduce false positive biopsies, thus, achieve high diagnostic specificity. A pilot clinical test on 22 lung patients demonstrated that using this system the malignant lung lesions can be differentiated from the benign lesions with both diagnostic sensitivity and specificity of better than 80%. To further reduce the number of false positive diagnosis and allow even higher diagnostic accuracies, we have also developed an endoscopic laser Raman probe for in vivo real-time biochemical analysis of the suspicious tissue areas identified by the field imaging modalities (white light imaging and autofluorescence imaging). Preliminary Raman spectroscopy results will be reported at the conference.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".