In vivo assessment and evaluation of lung tissue morphologic and physiological changes from non-contact endoscopic reflectance spectroscopy for improving lung cancer detection
Bibliographic record
Abstract
We present a method for lung cancer detection exploiting reflectance spectra measured in vivo during endoscopic imaging of the lung. The measured reflectance spectra were analyzed using a specially developed light-transport model to obtain quantitative information about cancer-related, physiological, and morphologic changes in the superficial bronchial mucosa layers. The light-transport model allowed us to obtain the absorption coefficient (mua) and further to derive the micro-vascular blood volume fraction in tissue and the tissue blood oxygen saturation. The model also allowed us to obtain the scattering coefficient (mus) and the anisotropy coefficient (g) and further to derive the tissue scattering micro-particle volume fraction and size distribution. The specular component of the reflectance signal and the instrument response were accounted for during the analysis. The method was validated using 100 reflectance spectra measured in vivo in a noncontact fashion from 22 lung patients (50 normal tissue/benign lesion sites and 50 malignant lesion sites). The classification between normal tissue/benign lesions and malignant lesions was further investigated using the derived quantitative parameters and discriminant function analysis. The results demonstrated significant differences between the normal tissue/benign lesions and the malignant lesions in terms of tissue blood volume fraction, blood oxygen saturation, tissue scatterer volume fractions, and size distribution. The results also showed that the malignant lung lesions can be differentiated from normal tissue/benign lesions with both diagnostic sensitivity and specificity of better than 80%.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".