Near‐infrared Raman spectroscopy for optical diagnosis of lung cancer
Bibliographic record
Abstract
Raman spectroscopy is a vibrational spectroscopic technique that can be used to optically probe the molecular changes associated with diseased tissues. The objective of our study was to explore near-infrared (NIR) Raman spectroscopy for distinguishing tumor from normal bronchial tissue. Bronchial tissue specimens (12 normal, 10 squamous cell carcinoma (SCC) and 6 adenocarcinoma) were obtained from 10 patients with known or suspected malignancies of the lung. A rapid-acquisition dispersive-type NIR Raman spectroscopy system was used for tissue Raman studies at 785 nm excitation. High-quality Raman spectra in the 700-1,800 cm(-1) range from human bronchial tissues in vitro could be obtained within 5 sec. Raman spectra differed significantly between normal and malignant tumor tissue, with tumors showing higher percentage signals for nucleic acid, tryptophan and phenylalanine and lower percentage signals for phospholipids, proline and valine, compared to normal tissue. Raman spectral shape differences between normal and tumor tissue were also observed particularly in the spectral ranges of 1,000-1,100, 1,200-1,400 and 1,500-1,700 cm(-1), which contain signals related to protein and lipid conformations and nucleic acid's CH stretching modes. The ratio of Raman intensities at 1,445 to 1,655 cm(-1) provided good differentiation between normal and malignant bronchial tissue (p < 0.0001). The results of this exploratory study indicate that NIR Raman spectroscopy provides significant potential for the noninvasive diagnosis of lung cancers in vivo based on the optic evaluation of biomolecules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 0.002 |
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".